{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多层神经网络，Sequential 和 Module\n",
    "通过前面的章节，我们了解到了机器学习领域中最常见的两个模型，线性回归模型和 Logistic 回归模型，他们分别是处理机器学习中最常见的两类问题-回归问题和分类问题。\n",
    "\n",
    "下面我们会讲第一个深度学习的模型，多层神经网络。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多层神经网络\n",
    "在前面的线性回归中，我们的公式是 $y = w x + b$，而在 Logistic 回归中，我们的公式是 $y = Sigmoid(w x + b)$，其实它们都可以看成单层神经网络，其中 Sigmoid 被称为激活函数，之后我们会详细介绍激活函数以及为什么必须使用激活函数，下面我们从理解神经网络入手。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 理解神经网络\n",
    "神经网络的灵感来自于人脑的神经元系统，下面我们放一张人脑的神经元和神经网络的对比图(来自 cs231n)\n",
    "\n",
    "![](https://ws4.sinaimg.cn/large/006tNc79ly1fmgiz5mqs3j30or0773zg.jpg)\n",
    "\n",
    "左边是一张神经元的图片，神经元通过突触接受输入，然后通过**神经激活**的方式传输给后面的神经元。这对比于右边的神经网络，首先接受数据输入，然后通过计算得到结果，接着经过**激活函数**，再传给第二层的神经元。\n",
    "\n",
    "所以前面讲的 logistic 回归模型和线性回归模型都可以看做是一个单层神经网络，而 logistic 回归中使用了激活函数 sigmoid。\n",
    "\n",
    "神经网络使用的激活函数都是非线性的，每个激活函数都输入一个值，然后做一种特定的数学运算得到一个结果，下面举几个例子\n",
    "\n",
    "sigmoid 激活函数\n",
    "\n",
    "$$\\sigma(x) = \\frac{1}{1 + e^{-x}}$$\n",
    "\n",
    "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmgj7yto7gj308w05oa9w.jpg)\n",
    "\n",
    "tanh 激活函数\n",
    "\n",
    "$$tanh(x) = 2 \\sigma(2x) - 1$$\n",
    "\n",
    "![](https://ws3.sinaimg.cn/large/006tNc79ly1fmgj8yjdnlj308w05mt8j.jpg)\n",
    "\n",
    "ReLU 激活函数\n",
    "\n",
    "$$ReLU(x) = max(0, x)$$\n",
    "\n",
    "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmgj94ky2oj308n05uq2r.jpg)\n",
    "\n",
    "我们下面重点讲一讲 ReLU 激活函数，因为 **现在神经网络中 90% 的情况都是使用这个激活函数** 。一般一个一层的神经网络的公式就是 $y = max(0, w x + b)$，一个两层的神经网络就是 $y = w_2\\ max(0, w_1 x + b_1) + b_2$，非常简单，但是却很有效，使用这个激活函数能够加快梯度下降法的收敛速度，同时对比与其他的激活函数，这个激活函数计算更加简单，所以现在变得非常流行，之后你会发现我们激活在所有的神经网络中都会使用它。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经网络的结构\n",
    "神经网络就是很多个神经元堆在一起形成一层神经网络，那么多个层堆叠在一起就是深层神经网络，我们可以通过下面的图展示一个两层的神经网络和三层的神经网络\n",
    "\n",
    "![](https://ws2.sinaimg.cn/large/006tNc79ly1fmgjiafmmjj30nu07075w.jpg)\n",
    "\n",
    "可以看到，神经网络的结构其实非常简单，主要有输入层，隐藏层，输出层构成，**输入层需要根据特征数目来决定，输出层根据解决的问题来决定，那么隐藏层的网路层数以及每层的神经元数就是可以调节的参数**，而不同的层数和每层的参数对模型的影响非常大，我们看看这个网站的 [demo](http://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html)\n",
    "\n",
    "神经网络向前传播也非常简单，就是一层一层不断做运算就可以了，可以看看下面这个例子\n",
    "\n",
    "![](https://ws2.sinaimg.cn/large/006tNc79ly1fmgj4q1j78g309u0cc4qq.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 为什么要使用激活函数\n",
    "激活函数在神经网络中非常重要，使用激活函数也是非常必要的，前面我们从人脑神经元的角度理解了激活函数，因为神经元需要通过激活才能往后传播，所以神经网络中需要激活函数，下面我们从数学的角度理解一下激活函数的必要性。\n",
    "\n",
    "比如一个两层的神经网络，使用 A 表示激活函数，那么\n",
    "\n",
    "$$\n",
    "y = w_2 A(w_1 x)\n",
    "$$\n",
    "\n",
    "如果我们不使用激活函数，那么神经网络的结果就是\n",
    "\n",
    "$$\n",
    "y = w_2 (w_1 x) = (w_2 w_1) x = \\bar{w} x\n",
    "$$\n",
    "\n",
    "可以看到，我们将两层神经网络的参数合在一起，用 $\\bar{w}$ 来表示，两层的神经网络其实就变成了一层神经网络，只不过参数变成了新的 $\\bar{w}$，所以如果不使用激活函数，那么不管多少层的神经网络，$y = w_n \\cdots w_2 w_1 x = \\bar{w} x$，就都变成了单层神经网络，所以在每一层我们都必须使用激活函数。\n",
    "\n",
    "**注：** 两个仿射变换的组合还是一个仿射变换。但是如果我们在两个仿射变换之间引入非线性，那么结果就大不一样了，我们可以构建出一个高性能的模型。\n",
    "\n",
    "最后我们看看激活函数对神经网络的影响\n",
    "\n",
    "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmgkeqjr34g306r065diu.gif)\n",
    "\n",
    "可以看到使用了激活函数之后，神经网络可以通过改变权重实现任意形状，越是复杂的神经网络能拟合的形状越复杂，这就是著名的神经网络万有逼近定理。\n",
    "\n",
    "下面我们通过例子来感受一下神经网络的强大之处"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from torch import nn\n",
    "from torch.autograd import Variable\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, x, y):\n",
    "    # Set min and max values and give it some padding\n",
    "    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1\n",
    "    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1\n",
    "    h = 0.01\n",
    "    # Generate a grid of points with distance h between them\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    # Predict the function value for the whole grid\n",
    "    Z = model(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    # Plot the contour and training examples\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
    "    plt.ylabel('x2')\n",
    "    plt.xlabel('x1')\n",
    "    plt.scatter(x[:, 0], x[:, 1], c=y.reshape(-1), s=40, cmap=plt.cm.Spectral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这次我们仍然处理一个二分类问题，但是比前面的 logistic 回归更加复杂"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "m = 400 # 样本数量\n",
    "N = int(m/2) # 每一类的点的个数\n",
    "D = 2 # 维度\n",
    "x = np.zeros((m, D))\n",
    "y = np.zeros((m, 1), dtype='uint8') # label 向量，0 表示红色，1 表示蓝色\n",
    "a = 4\n",
    "\n",
    "for j in range(2):\n",
    "    ix = range(N*j,N*(j+1))\n",
    "    t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta\n",
    "    r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius\n",
    "    x[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
    "    y[ix] = j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19bc2f5aa60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R/9jwM3bi4IiJ+zeLqC5Bbzl1KLoTytGr7+VvpM9LN1c78aJdh9jy7NdsfvJLjmxu+otvu9/6DXuFmQPdBmLX6R1OHECW0XQ69vUYQsbCLfVyb1lRGL/gdfxiw9EH+aML9EMxGWg1bRTj/nmZI5v2uT1PMRkI7dwSSacgKTJRAzsxccU7PifehPBGjNwCjBZClEuSpAdWSZI0VwixzgvX9lEPFGw94HGhsmBrMrayylop69UnzSf0J7JfB/LX70GtdMTPFT8Dga2b0fb68wDY/NSX7H7rdzSrDSEESe/8TuurxzL4k/ubbOzcnF+CAIqimoHkfhxVGhnrdrs3COvaisvTf+bw0q2Yc4uIGtiZ4LbNATBGhLgt/pH1Onq/cBPxE/ojNIGulhK6PhqOOo/IhYNjf3390f98ZXdNGFmn1FwZ6SYm2tDIisL4ea/R/83biezbgfCebej13A1MXvsBOj8j+Rv2svvt3x3dbVQNNIG90kLKj4vJnLO+sc33SPTQro7Pt4aPP25sr3q1QdYpNB/XlzbXjKt24gBdH7zMbYGVbNARP6E/ikHvc+JNFK9krUiSpACbgbbAh0KIpvtL8kF4jzboA/2wl1c575Alood0RR/gGtJoDGS9jo63XUDH2y5w2Zf8zXy32RT2CjP7v5jT5ESesjNKmPHzdvYaOqCNCCegrIiKoDA4qZ+mJEtMfO26RrGxw/TJFO1M4cAXc5ENjtoBnb+RcXNfRTHoT3G2j8bEK45cCKECPSVJCgVmSpLUVQix68RjJEmaDkwHSEhI8MZtfZwhkiwz8penWDjxcTS7imaxofM3ovgbGfr5Q41tXq2wV5hdO8MfxVZW5XZ7Y5GeWsSLj83DarE7UriDQpHsNof0r6ai6fRIqoqiyNz13zH4RzROI2pJkhj0wb10f+wq8tYmYQwPInZkjzqtffhoGLyaRy6EKJYkaRlwPrDrpH2fAp+CI2vFm/f1cfrEDuvOJfu+Yd/ncyjdl0Fk/460vX48xtBAhKaRNnMVB76eh2ZTaXP1WFpfMarWfR8bgoSLhnLwh0Vui1UMIQGNYJFnfvl6Mxazcyql0OkRqp2W+3dSERKOX0UZbW1H6DWwcUbjJxPRqy1BrZshyb4M5bOBOv8yJUmKAmxHnbgfMBZ4rc6W+ah3/OMi6fW0s+MQQrB02gtkzdtQ3ZE9b/Uu9n36D+cvfrPJTLHDe7X1KMB1eMlWhKY1GSe0f49r5SqAomn4V5bT6sAOgFr34awvSvZlsOyKFyjZl4Eky+hDAhjy2YO0mDigUe3ycWq88U1vBiyVJGkHsBFYKIT4xwvX9dEIZC/a7OTEwRHGKNyWTMpPSxrRMmcqUnM9ZtbYK6qc7G9sTCbPLz+d7XicP6xbq4Ywxy228ipmD7uHwh0pDmGySjNVhwtYevlzFGxLbjS7fNQOb2St7BBC9BJCdBdCdBVCPO8Nw3w0Dqm/LXfrBO0VZg7+sKgRLHJPcNs4j9rmukD/WglzNRQjzmuH3uAaZ5aEICzfUeau+Bnp04i9Sg/9shS1yuqiw6Karex8/edGsspHbWkac08fTQZZrwMPOdjyKXQ6GhL/uEjiJw5wUWzU+Zvo/ugVTSasAnDh5d1o1zEKvUFB0ckYTToMkqDn9hXICEI6JTBmxnMuejYNxZHN+9nz4V/uZzGaoGhXaoPb5OP0aDqrVz6aBK2vGkPyNwuwVzr/qHUBJtrdcH4jWeWe4d89zprb3yb11+UOMSgh6PrQ5XR9eFpjm+bEotl7Sdl/BEkCoQmiYwN54MnRhEVc65BHaIQXZN7a3ex47WfyVu/CUlwOHnTJJVlu1JCPj9rh01rx4YQQgrV3vcfBbxZgr7KAEOgCTMSN7c2o359tkqlo1tIKzHnF+MdHNbmClZWLD/Ltp+uxWo5X0io6mZatwnjmjYmNYtOhX5ex8qbXHYqTp/j5K/5GJq95/7T1d3zUD560Vnwjch9OOHKJ76HNlaM5+IOjFViry0cRN7Z3kwpXnIghOABDcNNKOQQoTc7ij283OTlxANWukZleTFpKIS1bhzeoTZrNzprb36mWPTgVw75+1OfEzwJ8jtyHC9LRbva+jvZnRllqDksufpqSfZkUj5nmUr0JIMsSOdmlDe7I89bvwVpSu2bK+mD/JjkD8+GKz5H7OCcpT8tl3+ezKTuYTczQbrS9dlyDCIFpqsqs0Q+RSjCWhE7orGZsJtf7apogLj6k3u05mZ2v/eSxItYdtemx6qPx8TlyH6eFpbicg98uoGBbMqGdW9LuxvMxRTS8Q6qJrPkbWXzJMwi7ima1k/H3Wra/+D0XbPiIgPj66b5zjDXfrGRx59EIQFN0SJoKQnNSOtTpZVq2DqdF4qmbd3gT1Woja4EHvXE3SLJM7Kie9WeQD6/hc+Q+ak3x3nRmD70H1WxFrbSg+BnY/uL3TFj6FhG92jW2eYAjBrzsqhedYsC2CgsV6Fhx1/tM+LP+yhxUVeP7uRmouuMFQEJxNI2QNBUZgWQy0LNvPDffNaje7PBon9k1T9yJo80jFD8jkiIxZubzTaaS10fN+By5j1qz4tpXHHrVR52BWmVFrbKy7KoXuTjp6yahAZ63NslJfyU7oR0pnfuiyQrrVIlDb67gxjsH4efnfQe1PykPTZKBk1L5ZBnJbuf8yr1M/vkF/AMaJ7NGH+RPYMsYyg669tqUTHpGfPcERduS8WsWQesrRmEMbxzxLh+nj8+R+6gVVXlFFO065HZEV5GeT1nKYYLbxDWCZc5o9uMZIrlxiRzo2h9xwgh5/cpD5KcX8Mx7F3n93hazHUlRcHHkgKbIDH/26kZz4gDCrhLcPt7Fkct+Bno/dwOtLhlOq0uGN5J1PupC08wn89HkEHbV44hbtVgpaCJt1qIHd6lumnGoY28nJw6AJJOSVsrOOVu9fu92naJR3RXWCEGrOH9ih3f3+j1Ph/X3f0jOsu3OGyWJ2KHd6PZQ0yqi8nF6+By5j1rh1yyCgBbR7ndqgpU3vU5xUmqD2uQOncnAkE8fRPEzYvYP9HCUxJz3F3v93gGBBi66sgcG4/GUPVkGo5+emx4d6/X7nQ628ioOfDnPUQR0IkKQu3IntrLKxjHMh1fwOXIftUKSJIZ++TCy0X1sWa20sP3lHxrYKve0njaKyWvf9yQZA5JEQWHdm0+oFiuVOYVO4ZxJF3Xh7kdH0KlbDDHNghg6qg0vvD2JhAbOUDmZysMFDhkDN8h6hYqsIw1skQ9v4ouR+6g1MUO6Ej2kKzlL3IclclbtbGCLPBPevQ2RYRvJL3ajkCgEwVrtKhvdoVptbHz4/9j/+RwQAsVkoMeT19Dl/kuRJInuvZvTvXfzU1+oAQloHum2CQc41hXqOy3TR/3iG5H7OC1K92d63NfUUtWm3TLAkcd9ErKmMnxS5zO+7upb3mT/53NQqyyoZivW4nK2Pv01Se/PqIu59YrO30TH2y9AOanAR/E30v7WyegDm0afVh9nhs+R+zgtalLqixnStEr6+w5JZMjAOGRVdeRxq3Zk1c4gYxFDnzyzxb2q3EIO/bbcJdZsrzSz/YXvEZr7UW9ToO9r0+l4x4Uo/kZ0ASYUPyMd/3MB/d+8rbFN81FHvNHqrQXwLRCLI+/qUyHEu3W9ro+mScLUIez54E+E3XmkK+t1dLp7auMY5QFJkrj1ifOYmFrIqp/XIyrNDJnSjRZ9257xNUv2Z6KYDGgW15CNrawSW1klhhBPi6yNi6wo9H/9P/R+7gaqcovwiwlD5+crwT8X8EaM3A48KITYIklSELBZkqSFQogkL1y7mopyC5vXZWCustG5RzPiE0K9eXkftaT7o1eQ8uNirEXlaDZHQ2HF30iLiQOI7N2+ka1zT/PEcKY9NsEr1wpMjHXrxAFkox5dI4YohBAUbN5PaXIWoZ1belQt1PkZCUqMbWDrfNQndXbkQojDwOGj/18mSdIeoDngNUe+eV06n7y1CkmSUDUNWZLoN6Qlt9w9GFlu/GrCfxN+MeFM3f4ZO1//hfRZa9AH+RM1uDNFO1L4o+P1xI7sSffHrjxnHUVgi2iaje5F9uItTg5d8TfS5d5LGk0t0HykhPnjH6F0fyaSLKOpKpG92zP2n5eapMSvD+/i1cYSkiQlAiuArkKI0pP2TQemAyQkJPRJS0ur1TXLSs08cMsMrFbnqbzBqHDd9AEMG+M9reSyUjOyLBMQ2LSaEzRlNj/9JUlv/1HdJkzSKej8jVyw/iNCOrRoZOvqB1tZJcuvfYXs+RuRjXo0q532t06i/1u3N5ojnzf2IXJW7kQcnSWBY4aQMGUwo355ulFsamqoqkby3nxUVaNthygMRvfj2PIyC/uT8jCadHToEoNOd3wpUVM1qqps+PnpkZWGX2Ks98YSkiQFAn8A953sxAGEEJ8Cn4KjQ1Btr7thlXuHb7WoLJy91yuOPHlvPl98sJa8nDIE0KZdBLfeO4To2KA6X/tcpjKnkN1v/Ip6wshU2FVsZVVsfPRTxv75QiNa510qMvPZ8sxXpP21Bs1mR9IEQggCWkTT9/XptJgwoNFsqzxcQO7qXU5OHECz2EibsZLKwwX4N4toJOvqH5tNZe3yQ6xblYrBoDBibFt69ot3qkRO2nGYD95YgWoXSJJDRvj62wYwZGRrp2v9/fsu/vplBzqdjECgKDL3PTGKth2jmPXrDubN2oPNqmIw6ph8SRcmXtSlSWgMecWRS5Kkx+HEfxBCeDUHq7LSit3uPhOgssJa5+vnZJfy+jOLsFiO/wgO7M3n+Ufn8uYnF2E6hbhSemoRM37cxsF9RwgONTFxahdat4+guLCKFi3DCAw+dxeTcpZtQzLo4OSYsRBkL6q9XGpDYy0p5+APiynadYiwbq1pc/WYGsMPVbmFzOx+C7Zi14YMxbtTWXrpc5w371VihzVOCb45vxjFoHcbuxeqxryxDzN126eOxtrnGFarykuPzyM7s6S6E9P2zVlExwZx0x0Dad85muKiKt5+aalLp6avP15HXHwIrdo6XnLbN2Xx16/bsdk0bLbjx/7v+cUMG9uW5QsPVF/Dbrfy5y87sNs1Lry8caUXwDtZKxLwBbBHCPFW3U1yplO3WPT6XU6OFkBRJLr3rrtI07y/kpz+aODQhbJaVNauOESHLjHM+HEbe3fl4h9g4LzJHRk9oQOyLLFiUTJffbQO7ahQf2mJmc/eW40kSxgMCnabytDRbbn+tgHnZCxfF2DyOBrRmZrmC6w4KZXZw+5Ds9qwV5jRBZjY8tSXTFr1HqEdE9yes/X5b6udeFFkLAc796UiKAy91UyLg7uJT0li4yP/xwVrP2zIR6kmuF18jWmP5em5pM5YSetpoxrQqoZh9h87SUspdNJy01RBTlYprz29kGbNQ8jLLXNx4gA2q8qCv/fwn/uHAvDbd1uwWV0/R7uqsmTufhcdHatFZc6M3Uy8qAt6feN2UvLGK3oIcC2wU5KkbUe3PSGEmOOFa9OmfSQdu8awZ1dO9R9DliX8/PVMvrTuecuHDhZUO+ITsZjt7NySxY9fbMJmUxECykot/PzNFlYuOUhEdACb12W4NK8VAoQqMFc5XjzLFhxgx5YsLry8G30HtiQw2MiqpQeZ8eN2Co5UEBrmx5RLuzJ6QocmMUU7HeLGuYTqAEdstu315zWwNbVj2ZUvYS0+LsVrrzBDpYXlV7/MhZs/cXtO6q/LASiMimNXv9FoOsfPxuoXwKGOvagKCEbZ2njNxHV+Rno8dS1bnvjCrUNXK8xkzl1/Vjryw1klrFh8kIoyC937NKdXv3iUo7Fpi9nG37/v8iixrqqCzPRij9cWAvJyHS/oqiqbx2PtNoEkub+JEFBcWEVUTOOmnHoja2UVUG8eSJIk7nl8JIvn7GXxvP1YzHZ69GnOhZd3Jzyi7q27mseHkJZShDjJmcuyxOb1rlWMNqtK6sFCUg8W1voehUcq+eaTDXz/+Sb6DU5g09r06pdScWEVP3+zhbJSC1Ov6FG3h2lgdCYDo/94jsVTn0JoArXKgi7Qj5COLej1/A0c2bKfXf/7jdJ9GUT260jXhy5vVKnb8ow8Sg9kukrxCkFJUiqV2Ufwj4t0OU+zOl7KyV36VTvx6n06PYcT2tGhKLW+zK4V3R6eRurvyynY5KpCKekUTJFNq4tTbVgybz8/frkJVdXQVMG6lanExYfw6Avj0FTBxjVpbgdhtUWnk2nfySEEt2VdBpIsIVTPDtsdNptKcVHl2e/IGwKdTmb8lM6Mn3LmZdWeOP/Czmw8wbEeoy5fEHdomkCzqqxZdshln9WiMnvmbiZM7YzR5Dkmb7WqlJeaCQ4xoWvkqdwx4sb05vK0n0j5eSlVeUXEDOpC3Lg+pPy8hFU3veHINRdQuP0gB39YyPmL/kdU/46NYqtmtTu64LhDllGtdre7wrq3Jnf1LiqDQt2fqqmEXOGdPPUzRZIk+r02nUUXPIm90uy0T9braHdj49oHUHikgrl/JbF/dx6R0YGMv7BTtSM9meLCSn78YiM22/EZhsVsJy2lkLuv/63691mXpDu9QWHcZMd3sbLSiixJaCdPsU+Bpglef3oRA4YmcvPdgxptVn1WOPL6JKFVOHc8OIzP31+L3a6iacIRSmngSmtZlsk9XEZCq3DSDxXy01eb2b8nD6NRx7AxbbDZVFYuOnj0WIkJUzsz5fLuTSL2bgwPptMdF1b/u+RAJiuufdXpVyZUDXu5mVU3vc5Fu75sDDMJat0MY1gQlZWuglmm6FACW8a4Pa/fm7cxZ9i9KHYbqt41NVXS6+h+U+OHkpqN6kXXhy5j5+u/gCQ55smaoP+btxHWJbFRbcvOKOH5R+ditaqodo3UlEK2bcpkyuXd3C4Wbl6f4dYpHhsQ1ZXAIAP/ffn86ll9p26xZ/xbslpVNqxJo1vvOAYMTayzbWfCv96RA/Tq34L3v25OZnoxOr3C43fNanAbVLtKcKgf2RklvPj4fCxmx+jQbrMyf9YewHn0MXvmbpAkpk5r/BXzk1l+5Yseh0rFSWlYisswhjZ8aucxKd4lFz2NarEhVA1JkZGNeoZ+8bDH0VT0gE4M/fJhUt5YQkZCezRFd8I1IaJZCK07NA31wF7P3kC7GyeQMXsdsk6hxZTB+MeGN7ZZfPfZBqqqbE5rSna7xowft5N6sJA7Hx7ulK+tqhrerHE5EUlyzMTjWhwPN8UnhNJ7QAu2bsjAUr0WByDVanZuMdtZPHcfPfs2R2/QNfgAyyeadRRZkUloFU5cfAixcfXnZGRZQlGc/8g6nUyHrjGEhvkx8+ftWE/K0BHC1S9aLSpz/9ztMTWzsbCWVlC4I6XGY/LX720ga1xpPq4vk9d/RJtrxhLRpz1trzuPKRs/Jm50rxrPa3vNOJ5Y9RQd24ej00mYTDpMJh2R0YE89PSYJrVQHdgyhk53XEiH6ZPr3YlnZ5awc2s2xYWeG1MIIdi7K9clMeAYOzZnMfOnbU7bevSJx7OgvDOSBDq9jKzU7nijSceIca7Nwv9z3xCuuLEPcfEhhIb7MWhEa66/rT9GU+3Guwf25HPbVb9w+9W/MOPHbWgeZIPrg3N6RG4x29i2KQuL2U6nbrG1XpC47Lre/N9bq1yqSU+F3qAQ0yyQ7r2bs3D2Pmwnna/Xy8TEBRMSZmJ/Uj46nYyqarQ8Gt4B2L8nr9ZxP1UVlJWaCQuv+6Kvt9Bs7uPM1cgSVYcLGsYYD4R1SWTYV4+e1jlWi53du48w9IJuXBjpT3FRFWHh/rTvHN2knHhDUVpcxf9eWEJGahFCOEIeIaEmxk/pxKjx7V16k8qK55Gt3a6xaM5+Lru2d/W2mGZBjJvUgcVz9rukHp+MopN5/MXz0BsU0g4WMHvGbo7kVTjCMJqjAEiSJWRZIio6kNseGEpwiMnlOrIiM/r8Dow+v0P1NiEE+5Py2bg2zW0K44kcez5zlSObJuXAER54cjSVFTbWrUqlrNRM+07RdO4e6/XvzDnryHdsyeKD11cgSSA0gabBqPHtuOrmvqf8EPsOTEC9ZzA/f72ZooJKt45VkqD3gBZ07hbL+tVp2G0qg0a0ZsS4thiNOqZO687BAwWsWnKQHZuzUHQyQ0e15oJLu2Hy05OTVUp2ZgnRsYHEtzzePSYkxERxLbvXSBIEBrnP19Y0we7th0nZ7yhU6j8k8ZTSA0KIOn/BTBEhhLSPpzjJfUWuYjQQ2jmxTvdoaJJ2HObdV5YDwuG0VI0xEzsw4IbEf6UTB3jz+cWkpRQ5bSspNvP791uZP2sPz/5vUnX8WZIk+g5KYMMqz1km5iobmiacQhLTru9D5+7NWDx3H+WlFoQQpB0qchog6fQy3Xs3p+3R0FbLVuEMH+sYbQshsFlVdHqZ/NwKhyM/zewSSZK49d7BJLYN56evNqN5yGo5GU0T7Nx6mKce+Ie8nHIQjli60aQjPiGUR18Yh9GDRMCZ4FWtldrSt29fsWlT/eXdlpdauP/WP1zeoAajwi13D671goQQAnOVjW8+Wc+WDZnVcWujSUePvs2548FhXv8hr11+iC8/WnvKt7/BqDD6/PZceaNrLre5ysYrTy4gJ6sUs9mOwaggSRIPPjWaDl1cF/Q2rU3jl2+3kne4jIBAA+OndOKCS7qesZZE7prdzBvzoGuloSIT2ac9k9d+0OgOUAhB1rwNJH+/CGFXaX3VGBIuGIQkOz9zRbmF+2+e4TIqNBp13Hz3oEZb3KpP8nPLWLPsEBUVVrr1iqNLj2ZODjY7s4Qn7p7lceYoSTBgaCK3H51lgqNY7tmH5lCQX+H2nNjmwbz24YVu9x3DblP54oO1bFiThl6vYLdpdO4Ryx0PDjtlBXZd+fy9NaxcctAr19LrZcZP6eQ0A6kt9a610pRYvzrVbTzOalGZPyuJ+Jah6HQKMc0csfD0Q4VkZZQQ0yyIVm0jqp2MJEn4+Rv4z/1DSdqRw9rlhxAIBg5rRdeezerFGQ0cnkjaoUIWzt5bvfgjyxKt2kawPykPRaegqhpDR7fh8uvcfxF+/34rmenF2I+mbh17Kbzz8jLe//pSp9TFDWvS+Ozd1dXHVJRb+fv3naxZloJerxCfGMaUy7oRF1/7POSYwV2Yuu0zVt/+NnmrdoEmkBSZhIuGMOT/HmgSTnz51S+R8ffaarGvzLnriR3ZkzEzn3cSvlq9YC92swUk53RPi8XOvFl7zjlHvmJRMt9+ugFNE6h2jWULDpDYJpyHnx1bXb1YmF9RY/hPCNiyIcNpW3CIidc+upD/Pb+YfbtzObFuyWBQuPLGPqe0TadX+M/9Q7nixj7kZpcRGR1AeGTDKDvWNNyVZMmlDqUmbDaNFYsOnpEj98Q56cjLSy0uZffHSDlQwHMPz0VogrAIf0wmHYezS5EkUO2CoBAjN94xiO6945wcepcezejSo1m92y5JElfc0Ifzp3RiX1Ie/gEGOnWLRaeTKS+1UHCkgqiYQJcY5ImsWppS7cRPRNMEe3bl0q3X8aKcX77e4jL6t1k1crLLAMhIK2b9ylTufHgYfQe1rPVzhHRowcQlbyE0jaq8YvRBfugDmkY7sewFm5ycOIC93EzO0m2k/7maxEuGAw6Hv/XdWaih7kv3S4vr3sC5KVFSXMW3n25wCl1YzHZSDhSw8J+9TLyoCwBxtegFILmpEdTrFR55dixz/0pi3l97KC+zENcihGnX9z6tHqchoX6EhDbsd2ngsEQ2rkmrnpUfw2jU0aFLNLu2HT6t2hOrh5qFM+WcdOTtO0djMOpcPnRwjBaObc89XOayv6jAIbDTum0EDz83Fr96nrJ5IjTc32W0FxhsrJUI18mLrCdirjoe7rDbVAryXYWgTkbTBJ+8tYpPfmrhlCJWGyRZbhLpbyeS8tOSaicugPy4RDJbd8ZqMJH23XZuG9abyOhA8tbsxpSSgtyjOdpJzy0Jjc7dzi3N9U1r092WaNusKssXHqh25OER/oSEmSgpMrs5GiTZsX7kDlmRmXRxVyZd3NVbZjcIXXs2o0ef5mzfnOUUYm3XMYq7HhnOe68uZ9e2w7W6liRB157erXA+J9MPO3aNIbFNOHrDmVU/Ck2QllLIj180nn5GXejY1X1hi92u0qHz8Uo6RSfXOrXKZtM4dOCIV+xrShzs0o+9PYdQGh6NOTCYZEJ46v5/yD1cSsnedMKK8ggsKURSTxgUaBqyqjLl8qbVo7Su2Gyqx1Gl7aQZ3iPPjkWnd3UfiiIRGurHFbUIlZxNSJLE7Q8O4/YHhtJ3UAK9B7Tg1nsG8+BTozGa9Dz0zBj6D0lAf8JnYjTpCI/wd1rUlBUJk5+ey6+rOd31dDknR+SSJPHQM2OZPWMXyxYck54UVFa4b9HlDrtdY83yFG68fUCjCMjXhStv6ssLj87Fajn+wzQadZw/tTPBJ0xJJUlizIQOLPxnb61SLRtjYbw+aH3laFL/WEG5ppCd2MGpwEcgUVVp47fvt3HpgHgkWaLH2gWkte/O4aPFQGH52fRUc4mKuakRn+L0KCmuYsPqNEqKqsjOLGbvrjxkWWLAsEQuvrIHAYFGuvduzu/fb3M5V6eT6TfIObwU3zKMN//vIhb8vYdd2w5jtaqEhpnoO6glQ0e3abSZbH0iyxK9+regV3/X2YYkSdzx0HB2bs1m5ZKD2K0aA4cn0mdgC7ZtzGLerCRKi8107hHLBZd0IyLKu7H9czJrxR1fvO9YdT6dx5UkiU9/uRJZgpVLDjpWrQUMGdWa4WPbNrp0ZU3kHi7jn993snd3LqHh/ky4sLPb6a7drvH5e2vYtDYNRSdXqzaejCTBl79f7falpqkaG9ems3xhMna7yqDhrRgyqg2GM5wR1TdCCJZd+QIbtuSxt31vNJ2r0/Hz1/PxD9OY2e1mSvdnOjWbVvyNjPjuCVpeNLQhzT5j1q44xBcfrEVowqWATNHJREYF8OK7F2AwKHzzyXpWL02pztLR6WWCgow8//Zkt7nXPhoWT1kr/xpHnp1ZwlP3/XNalZDN4oN56d0LePWphaQeLKheFDQYFVq0DOOJl85rMuJVdaUgv4KM1CIWz93Hji3ZLvunXtGdi9yoMwoheP81R3zwWOzQYFRo3iKEJ14+v0k783kfLOKPxdnY3EQYQ0JNvPf1ZVTlFrLsyhfJW5uEYtCBLNP35VvoePuURrD69CkuquKh/8yscd3EaNJx7a39GTamDUIINq/LYPGcfVRUWOnVP55xkzp6rFf4t2GzqRTkVRAUYiQgsOE/k39V+qE74uJDaNY8mIy04lodrzcoXHtrfzavyyAtpdAps8NqUclML2bDmjQGj2hdw1XOHiKiAoiICqB7n+b89dsO5s7YjcWiEhhk4PLrerstaQZI2pHj5MTB8fkcSi7k03dWcceDw84oNGUtKacyu4CAFtHo66EzvSRJjL51JDNX/w4nLYrr9QrDjz6vX0w4E5a8RWVOIdbCUoLaNkcxnD1hg41r0k4pEWgx29m5NZthY9pUF+/0HeQ+U+ffihCC2TN28/dvOxE4tGB69ovnlrsHN4kwkrdavX0JTAbyhBBNdjm6JonYE5Ek6NGnOUvn7yd5b77b7BeL2c76VeeOIz+GLEtcNK0HF02rnTb6prXpbj+fY/s+fXcNtz0wFIvFTk5WKSGhJkJrkBRQLVbW3vkuKT8uQdYraHaNDtMn0e/N27ze2Nho0nP3oyN479VlCOHIzpAl0CsQFR2AzaZWh8/8Y8ObXPZNbTBX2U45C5VlidCwf3fYxFxlY/WyFA7szScmNpDhY9s5xbGXzNvPX7/ucBrQbduYyfuvLueR58Y2hslOeGtE/jXwAfCtl65XLwwe2Yr01MJTVk0KAVs3ZKCeohzXmyW2Zys6veyQQXDzUQkBm9al88PnG1m+MBlJllDtKu07R3PHg8PdplKu+c/bHPptOarZino0u23fZ7NRjHr6vjrd6/Z36xXHYzd15P+emU1edAs0JCrN8M37q5n7526eeWMifv41Sxs0Zbr0aMafP++o0ZkrOpkR57mfcf0bKMiv4JkH/6GywoaqCmRFYs7MJO59YmR1muBfv+508Rt2m8b+PXnkZJcSGxfcGKZX45V0DCHECqD2LXMaiWFj2hIXH4KhFg64Nk58+Ng23jLtrGXQ8FY1pnkKTTg6O1nsmKts2Gwae3fl8eYLi8nKKGbxnH2sXXEIc5UNS2EpKb8sRa1y1gtXKy0kvTeDgu3JXrdftdpYcd0r5EfFI2TlmHYpqqyQk1XKR/9b6bGs3BtoqkrW/I3s/b+/yVu72+uZQa3bRdK1l+ecZUmG66b3p3mLUK/e92zinZeXUlZqrf7Na6rAalX58I0V2O0amiYoKXJf/KXTyeRmu9ajNDT/qiGlwaDw5Kvns2ZZCutWpqKqGgf3HTmtBVBJcsTPh4xq1SCVnk2d1u0iGTOhA3P/THK7365qLvXNqqqRerCApx+YjSRJKLLEF5pg1MBmVERE4Zftutiqmm38M+huwru3ZtzfL2GKCvWK/YcXbyEvLNbtlEIgsWNzNo/c8Sejx7evleDa6VCWmsPckfdjLSpDs2tIskRo50TGL3gNQ4j3Wofd+9gIvvxoHSsXH9cKUXQSQcEmnnp1PJHRDa8N31QoLzWTfqjI7T6L2c7Bffl06BJDSJifW2dut2vE1KPsdW1pMEcuSdJ0YDpAQkLjLaTo9QojxrWrXrzbvf0wX3ywlrISM5oQGE06Ksqsbs+NjQumz4AW9BvSklZtIxrS7CbNFTf0wWZzdBp3KSjxMMAUGtiPCm4cy+5fsCITuddoAtoU0X3dIvQ255G5ZrZSsPUAiy96mkmr3vOK7bZyM+IULWftNo3lC5Np3S6SQSNaeeW+AIunPkVl5hGnhsmFOw6y9s53GfH9f712H1mRueXuwVxwaTdWLEqmpKiKLj2a0W9wwjmTdXWmbN+S5XGfqorq7/OFl3fj5683O4VXdHpHz8/GDqtAAzpyIcSnwKfgSD9sqPueii49mvG/Ty+iIL/CoWecUsgHr69w1VQw6bj8ul70GehbzXfHVTf1paSoiu2bskByLKDJsozRqFBUS1leAE3RURYSzs7+o+mxbgGK6hyXFDaVgq3JlCZnEdy29vocnogd3o2I3HcQ7WoWMLJY7Mz9K8lrjrxkXwalyVkuXe81i43U31cy9AsritG7sfmYZkFcdq13KwrPJjRV49DBQkCQ2CYCRZHR1JpFr9oclccdfX57qqps/P3r8ayVXv1acPPdgxruAWrgXxVa8YQkSURGO6ay3XrF0bNv8+qGFOBw4l16NHNb0eXDgaLI3PXICLIyijmwJ5/AYCM9+jRn09p0vvzw1LK8TsgKpeHRrB5/JQkHdtDywA6nMbNs0FG0JxXVYiWoTRw605nn8/rFhNN6THeydmwjrX13NEVxBI7dUFbqXlvkTLAUliLrFNx+KkJgr/K+I/+3UF5mYf3KVEqKq2jTIYpuveJI2nGYj99ahf2omJ5OpzD9viF07dUMRZawu3HkHbvGVNdBSJLE5Iu7Mv6CThTkVxAUbDqlvn9D4q30w5+AkUCkJEmZwDNCiC+8ce2G5pimwo4t2axZloIQMGh4Ij36xjeJRsdNneYtQp0WzgYNb4W5ysbv32/DYrEjhCAk1I+igsqa1eIkCU2nI71dN/RWM83T9lfvspVUsOTCp48eBwlThjDq16eR9Wf2dS7ankLLg9mEFOaS2r4HxZHNqhc9jyHLEp26ek8kK6xba7SjTkUAJeHRlITHoLeaaaWrwhDSMPKs5xp7dubw9otLEQisFkcjh4hIf47kV5w0mLDzwevLefGdyUy5vBv//L6rWqZCkiQCAvXc9oBr5a5erzSJUMrJ/GsqOxuDzevS+eePXRQcqaRVm3AuurIHiW0csXVNE+zdlUPhkUoSWoeTkBh2iqud3WiqRnGxmYAAPcVFVTzzwByqzLaahZ6PYqiqYPDC32o8JrJ/xzNuWPGVMrZ6sVMA2waPpyw0Ck139MUggcmk4/m3JhHT7Mx/xCd3YNrx+s9sefEHtnQfRlloJJqsIGsqisnA/U+N8S2mnyY2m8o91/9GZaWzptKxj/xkV6coEmMmduDqm/uRtOMwC2fvo6Soiu694xg7sWOtlEYbmn99ZWdDM3vmbv78eXv1KGDb5iySdubw4NNjiIj059WnFjraV+H4gbfrGMV9T4yqVWrk2YisyNWtv2Ka6Xn2fxP544dtbN+UWd213BNWkz9IEoqfAbXS4vaYIxv2snr6/xj62UOnbZspOhRzriNzQQK6r1tEWvvu5LRsD0EBdO7ejGnX9zkjJy6EYP8Xc9j2/LdUZh3BPy6Snk9fS/tbJtH9kSvYWOpH2Y4iNNkxhddkGc0ueO+VZbz/zWXn7PehPtiwOs1tf09PY1VVFeRklwLQuXszOnc/e1+cvm9JPVBVaWXmT9ud9S2Eo3T964/WIUkSBUcqnRZY9ifl89NXm7n+tgGNYHHDExsXzJ0POxo4rFt5iF++3Uqhh3ztiOhArrfMZ+tz37DjpR88XvPAl3NRrXb6vXor/s1qn1XU/ZEr2PLUl9iPviQUTaXNwR30C6hgytyPT+OpXNn91m9seeorVLMjE6oy6wgb7v8YS3EF3R+exvYsW7UTd0KCHVuyfaXytSQ7o4SvP153yvqPE9HrFdq2j6pHqxqOs0uf9SzhUHIBiuJ+in84q5T83DKXVXKbTWXl4uRTdhkpPFLBikXJrF6WQkW5+zTJs42Bw1rx9mcX06VHMxeNa4NR4eIreyDrFEI7naJDkYCUHxYxs9vNlB2qncg/QOf7LqHDHReimAzogwNQ/AxE9e/EuNkvn8njVFOZW8imRz+rduLHsFea2fHi96gWK1YPHeKFgKrK2ssu/9v59v/W17igrigSJ0bdHPUgMqPGnxsVrb4ReT3g529wEeI/EU8FSDabhs2meiz9n/HjNubM3I0sS0iSxFfaOm69p/bNpJs69zw2gq8/Xs+mtWlIsoRe73DiQ0c7KmgTLxnG6ulGj+EVADSBrbiCTY9/zqifn6rVfSVJov/r/6HHE1dTnJSGf7NwglrVfZq99LLnXNILjyGEoDw1l87dY9myPsNl+q+pGp27n1sdiOoLTdXYm5Tncb/eoHD3oyOYPWM3B/bmgYC2HaO48Y6BTvr8ZzM+R14PJLYJR5Zwn1qG55idJEFGahFtO7hO93ZuzWbeX0kuL4jP3ltDm/aR1emTZzMmP0emgLlqABXlVkLD/VBOUE5UjAambPqEv/r8B63K82xEaBqZc9ef9v2NoYHEDO5yRrafTGX2EY5s3Odxv2q1YYoK4fJre5O0IweL2e7UBGT42LZebz7QVCgtMTPjx21sXJMOwIChLbn4yp5nvrgoOTqEuvtZKYrErfcMokef5vTo07x6BnSurT2cW0/TRJAkiZ794qu/qCciKxKahzieTq+Qk13q1pEv/Gev20VBoQlWLU3hgku7svCfvSyZtx+L2U73Ps2ZOq37WekMTH56TB6kQUM7JnBN4V/8PeBOivekITw02T5TqVnVamP/Z7M58NU8NFWjzdVj6HTHhej8T60OmL10K0nv/I65oJSwrq1RDDo0i/vwSNzoXhjDg4kFXnh7ErN+3UnSzhyCgk2Mn9KJgcMSz8j+pk5VlY1nH5pDcWEVquoYlCxfmMyOLdm89O7kWiuUnogsS/To25ztm7JcQpMmf71T0/BzzYEf49x8qibA1Gnd2bYx02UErdPJhEb7k+em8bMsScTFh7i9XmmJ+2IUu12jtLiKd19exp5dOdVxwlVLDrJ5fTovvj2Z8Mizz5nXxLGR+aFfl7Ly+tcQqvNnLBv1tL3uPIQQ5K7YwYGv52OvqCLx0hG0vHgYss59Wbqmqswf9zAFm/dXL3xu3Z/Bwe8XMXndh+hM7gtAVKuN2cPupeCEEXj+uj3gYb1D0ikM/+GJ6n9HxQRx892DT+szOJuwWlXycsoIDjGxflUqZaXmaicOx77DZtYsP8So8e3P6B7X/WcAzx2YQ1WlDYvFjk4vI8sSdz403GlWd67ic+T1RHzLMG69dwhffLAW+dgqiwS33T8Uu6rxf2+vctZt0MnEtQjxqOHSvXccmWlFLi8Go0lHeGQAq5akOF1P0wTmSht//77rnMyEkXUKba4aiyE4gKXTXkBoGprFhi7Qj+A2cfR69no2PPAR+z+fg73SghCQPm8Tke/P5PzFb7odsWfMWkvB1uRqJw6gVlkpO5hNyvcLaX/LJJdzhBDM7HoTZcknCX0ddeKSIju9aBSTgf5v3Y4prOkVlXibY80YZv22E0lyOGyjUed2UdJicTS3OFNHHh7hz+sfX8ia5YdI3ptPVGwQw8e2rU55PdfxOfJ6ZMDQRHr1b8GBPXlIErTvFF0tUlRxSz9+/XYrVqsdTRV06x3HrfcM9ljQMnZSR5bOP4BaZqmePur1MjHNglBVxyLpyaiqYEcNokDnAi0mD+LSg99z8LuFVOUWEju8B/GTBlC0I4V9n83GYtU40GMwec1bI2SJgIpSdG/8zfj/XuxyrdSZK7GXu1G4qzBz6Lflbh35rv/96urET0SWMUUEY84rJqBFFL2ev5F214+v0zM3FFVVNtatOMThzBLiE8MYMDTxtDT4ly844NKMwW5zv7YhyxAaVreFR6NJz6jx7c/4ZXA243Pk9YzBoLit0Bsxrh1DR7eh8EgF/gHGU+o2BIeYeO6tScz8cRtbNmSi6GSGjmrNlMu6sXpZCjq97Hak4x/QdPQg6gv/2HC6PTzNaVvq7yuwm21sGzKRiuBQxNHuQhWBofyyoZR2B47Qul2k0zkVGfke76EPcj+yS3p3Ro226UwGrsz5A6FpSPLZM8XPTC/m5SfmY7Op1aXuv327ladeO5/o2NrJtrprxuAJRZEZ+S90wN7C58gbEUWRiYqpvZZxeIQ/N989mJtP2t5/SEt+/mqzy/FGo45xkzvWeM3KCiuL5+xj8/oMwiP8ueCybueERK8QgpLwaKqCQhCK89dcRWbGj9t56JkxTttLklI9Xq/5hP5ut9tKa246kThtJMBZ5cQBPnxjBRUV1upUEIvZjtVi59N3VvPkq+fX6hpFhZW1vl/fQQnnvExFfeJz5OcAQcEmbn9wKB//b5WjnZqqIUsSfQcnMHSU5y5GxUWVPHr7X5iPqjweSi5g8/oMLr6qBxde3r2hzK8XEi8ZzryZu9DcKRlKEqkpzg2thBCY80vcXks26DBFhbJk7j7+/mMXJUVVRDcL4rJrehHZvyOHF291e54uyI9+r95a52dpaPJzyziSW+6SzyeE4ztSXmqpVapgeGRArbormUw6eg/wVbDWBZ8jP0foPSCBd768lM3r0zFX2ujSs9kp23d9+PqKaid+IjN+3M6o8e0JDqk55U4IwfpVqSyes4+qShu9B7bgvMmdCAxqfLGhyD7taTW4Aym5GiquWSonL4JJkoRfswiqDhe4HCspCqvS7CxbtaU6D/lwZimfvLWKS66cirI2yaVIyRgRzKWpP2IIOPsKTqxWFcmD0qckSdjstQuXXHxlD745RcXlsWv26Ft3bfl/M2fXfM9HjQQEGhg+pi3nXdCpVj0YD+z1HBNe+M/eU57/+ftr+PKDdezfk09GWjGzZ+zmyfv+cdLtzs4oYdPadNIP1dzSVQhBVaUVTa19271TcfnHt2IKMnHy0NJo1HHBpV0BsJVXsfbu9/gueDJVOQVwkgOTjXpCB3Zh6cpMl3J6q1Vlzsoczpv/OpH9OyIpMvogfzrfdwnTMn/xmhMXQnAouYBVSw6yf0+e1/t6nkyzuGAMRvcpmmERfrVelBw6ug2XX9sb/wA9BqOCTi8TFuGHXi+jNyiY/HSY/HTc/+QoXyPzOuL79P7F1OQPyspqbqKQfqiQDavSqjWcwdESrazEzNw/k5hyWTfefWUZyXvzUXQyqqoRnxDKg0+NcZmWL52/nxk/bqei3IJOrzB2YgcuubpnnfN/9QYdT74+ibdeXEJpiRlZlrDbNS64rBv9BrdECMH88x6mYGvy8cKdox+KpFdAQGinBBIeuQ7dN7udRdCOUlFmIbBbOy5Y9+Fp2Za8L5+M1CI6dYslNi6YnKxSVi87SEpyAf4BBvoNaknvAfHs3ZXLp++spqzUgqJz5EZHRAbw6Avj6pzl4QlZkbn5zkF89OZKbDYVIRxFNzq9zE13DjotqeBxkzsy6vz2FBVUEhhkwM/fQGZaEft25xEYbKRnv3ifE/cCPj3yfzF3Xvcr5aXudUsef+k8OnaJ8Xju7Bm7+OOHbW7V5qJjg2jTPpKNa9Own5D3rigynbrF8PCzY6u3LVtwgB++2Og0/TYYFQYMTeQWN0UyZaWOF8WmdekYjTpGn9+e4WPbkn6oiNkzdpGdXkzLNhFMurgL8S0di2eqzc6qH9eyPakQv9hw2jb3Jyw1maqMXA58NQ+10kJ5UCj5zRKxGQxosoLdYCSwtIj4nINoip71w6egCvcOzN9fj1EniAozYtebCAoxMXpCe3r0ae7i9HJzynjuwdlUVByv+PTz1zuV6IMj28lg1FFRbnF54cqyRNsOkfz3ldotOp4paSmFzJ6xi6yMElq2CmfixV2ITwit13v6qBlPeuReceSSJJ0PvAsowOdCiFdrOt7nyJsGG9ek8cHrK1y2x8YF89pHF9Z47oJ/9vDrN1vd5q/HtQghL6fMyYkfQ6+XefP/LiI03B8hBPfc+Dulxa6jf71e5q3PLnYSNSovtfDkfX9TVmqpFh4zGBWax4eQcagQu6o52rRpGjqdzIPPjiWyopCfr3+frR0GOLTfFR2K3YbBUkWf1XPQmc2kdOxFZusujjZvbojMTsXiF0BZaKRL5yAnhKjuYmA06hgxri1X39LvhN2C2676GXOVe8XD00GvV3jtowvPSgkGH2eOJ0de5xi5JEkK8CEwAegMXClJUue6XtdH/dNvcEtuf2ioY3HyaMPkPgNb8Nz/Jp7y3BP1K07k2Gha9jD9liVYPP1dZna/hblTn6asyH2Kmk6Ryc4sddo2d1YSZWUWJ/VIq0Xl0MEC7BrHe23KMnYNPnl5EXMnPM62dv3QFF11GqKq02M2BZDcoTclYVEOJ67TOZywm/+OxCVi8QvAv6wY2W5Dttvcx6VOeGaLxc7SBQfIzjyeCbNjS7ZXnDiAopMpL3PMpuxVFvZ/NZf1D3zEod9XoKm1W4z0ce7gjeBUfyBZCJECIEnSz8CFQJIXru2jnhk4tBUDhiRirrKhN+jQ6Wr3bg+P8OfK63rw41db0DSBhoTRqNCuUzQTp3Zmwd97nOLnx7BVWSmavwzFbkfsSkWe2ArVzT0tlRbCQp1j6VvWZ7gd5SMAN++NsgqV3Aj32RBCUchr3srRkcfDSLwaScJq9CM2/QCRuRmkt+3GkWan0EbHIWi2bVNmtX5O2sGaF3xPByEEzZoHU7D9ILMH3VWteZ70zh8YQgO4aPdXp9Vcw8fZjTeyVpoDGSf8O/PoNickSZouSdImSZI25ed7zpbw0fBIkoSfv8GtE9dsdor3pFF5Ulpe8d50cm96kgEr/6Zl0hZapibRfdNSbr2iPQajjsuu7eWS+SCrdhL270CxO0alEoK4lD3IdudRqqSqhBQfoWrDTqftnhQRPSEkUK2eR8BClslr3sppJO0RWSYvvg3BxQUEFx8BDzrjJyJJktNn2rGr5zUH9wZ6UMnUyUy9ojt6g8Lckfe7NK6wFlcwe+i9bs9VLVZSflnKjld+JH3WGrRaphL6aNp4Y0Tu7lfg8g0UQnwKfAqOGLkX7uujntn/1Vw2PvgJdrMVzWpDH2Ci/fTJdH/sSpZf9RKWwjL0QtCy4KiTlySWX/0yF27+hFHj22Py0/HHD9s5kl9BSKCe2A2biDmwy+kerfZtxWY0kRvfBllTEbJMcGEeXbasoHS/83hgzIT2ZKUVu+3LiNCOh1YANI3g4gJiKo6w311cW2gIpNo58eMnARCddYhDHXrWpm80fQceL3Rp3zma0DA/iotO0nM55rBPtEXTkIWGpNNx4nqywahw4+0DGTyyNfkb9mIrcV9wU37oMOYjJZgij6tplhzIZM6we7FXWVArLSj+RvyiQpm46j38Y8Nr8TTO2MoqqTxcQEB8VK1kfn3UH95w5JlAixP+HQ/UoCLko7ERQnBk0z4sBWXEjuyOzuRawJMxZz3r7n7fqdDFVlbF7rd+I/mbBdjKKl1HjEJQsieNymxHk+FBw1szaHhrwOFE/ur5jUuzDVkIOm5fQ+s9m6kMDMVYVYFfVTm6ID+C28c7HTt4RGt2bslmy4YM7Db1aHqiRMeqbPZooY4wiU6PbLOh0+xMbK+jqiicNvu2crB9TzTlaBxcHI3FeCh6cYek2onJPAiAn81MhwNb2dep3/GWfceuK0kgNPRGPdOu7+0iIfzy+xfw8hMLyEwvrj6thclOWUY+JWEOHXpjVQUtkncTU3yY6LceZdu+EmRFYsTYdgwe2ao6LbM8LadGm8tSDzs58qWXPuuoXj36d7OXVVFeZWXVTa9z3pwa8xOqsZutJH89j+0v/UDl4QKHiqQk0fGOKfR99VbkU4WpfNQL3nDkG4F2kiS1ArKAK4CrvHBdH/VA7qqdzJ/wKGrFUQctSXS4/QIGf+A8Fd/23DfuW6oJsBSWeh7JypLLVB8guG1zAhKiKd2X4eYkMFgtGApzHSYpMoaQABIuGOR8aVni9geHcSi5gF1bszEYdfQb0pJAncb8S55nV3olVUGh+BcX0Kd3M0Z/+Bj2KitRD35M4NxlJLfr6cg8OfrcHjnxBSVJ6BSJiIgABkkBmHWJRA/uwtipQ1j+329JUoMpDotCUjUCKksJbBVLm7E9GTm5i1tt+YBAIy+9dwFVlVZKis1ERQdgLSzlr17TsWwpq85n1wWYaH3laIbcPJgpHsxsNrqX52eQILTD8dlA6cFsSpOzXV6+wq5yeMlWbGWVHoXBjmGvsjB76D0U7UxBHF1wPva33vPhn0iKTL9Xp9d4DR/1Q50duRDCLknSXcB8HOmHXwohdtfZMh9ex1pSztxRDzg3YhCCfR/NIigxlm4PHVcQLEupoXmxJtwH1ABjeDCBbvpdSpLEqF+fdsR0LTaPfTclvUJUv46M+PG/yHr3X89WbSNchL0uWPQ6I1OyKTuUQ0iHFgTEO0a3kixzpNjMoTbdHE68NqGUk46RFZnn3puKX8DlAFiKyvi9zTVYi8tpe8JxugATU376hJD2LTgVfv6O4hgAv+gwpm7/nN3v/EHGrDUYwgLpdOdUEi8bUeM1TBEhxI7qSc7SbS77Wkwe5OSYbaUVyDrFfftBScJeZTmlI9//+WyK96ZXO/ET0cw2dr3xC/Hn96fZyJ41XseH9/FKSZUQYg4wxxvX8nFqNLuKpbAUY1iQR2fnjp1v/OLSTecY21/83smRB7eLJ7/Ac+KRPtgfzWZHs9gQqoakyMhGPUM/f8hj5V94t9ZcnvoTKT8tIX/jXvJW76oeJZpiwuj76q3Eje1zRvFagKDWcQS1jqv+txCCr+7/iRW2BAivRTz8hDxwJ+wq+5Ly6NnPEepJ/nYBqpsWbqrFxu63f2fwx/eftu2myBD6vHgTfV686bTOGz//dZZc9iwZf6+tzt5JvHg4I07oQAQQ2iXRYyjJv1kEpqjQU97r4PeLauyVioBFF/yXSw9+j6WglH2fzabqcAHNx/ej1bRR6PwaX4PnXMVXG9uEEUKQOWc9yd/MR7OpJE4bSfmhw+x8/Rc0iw1JkWl/y0RaXjwM/2YRBLetWXioYGuyx322Mud87l7PXc+iqU+5/eHKRj3tb5pA2xvGs+vNXynakUJY99Z0e3gaYV1b1WiDPsifDtMn02H6ZACspRVoNjumCPct7s4Uc5WNZx6cTU6WqLmI5xia5tHRq1Yb5aXHFyiLdqSgVrnOKMw6I+v2V5D/yw669GxGm/aRp1XOfibIOoWxM1/AVl5VvTahD3Qt3VcMega8cydr73z3+GxIklD8DAz++L5a2Sl5aJF3IkLVWHf3e2T8sx7NZkfYVTL+Wce2F76jzTXjKNyWTEiHFnS87QKnl66PuuEr0W9CFO06RPK3C7GVV9Ji8kBSflpC+p+rsVc4Kh8lveIYUZ/UC1LS65B1CqGdEhg983kCW0S7vf7WZ79m2/Pfud2nC/Lj2pJ/nLYd/Gkxa257G3vZcSemCzARmBjLpNXvYQhumlWFQgieuPtvp2KcGg4GIDpIwZpxmOLQKOfsF0BWVV54ZQzxXR0hk93v/sHmJ75wcuY5zVuzv+dgJEVBkyQMBh1dejbj7keGI8lSvTv02nJ46Va2v/QDpclZhHVvTc+nriWqX82a9cfY/8Uc1t/3YfX30SOy7DY9U1IUhKoi63XIeh2jZz5H83EuRYo+aqBeS/RPF58jd2XH6z+z7blv0ayOUIXiZ0Cz2BG1yFc+hqTIBLaK5ZK937htZGCrsvBDyAUIN7nDvV+6mR6Pu65Ra6pK7urdZPy9BmtxBXFje9PyoqFn3KW+vqmqtPLiY/PITD+1E5ckhxb2nY+MoG1zPz7vcCtbh0xElZXjo3hVpVlOKi+tegLF6IhpnxgjB7AY/Vg/9hJHVswJ6CRB26RNxCbvxhQdRvzEAXR/5ApCOpw6ht4U0Wx2Fkx6nLy1u48vlp+EbNQjhEDUkL9/DGNkCFcc/s2X6XIa1FuJvo+6U5qcxbZnv0GtslTHsNUq62k5cXBMa6tyishZscPtfr2fkcnrP8QQesJIWpJof+tEuj92pdtzZEWh2fDu9H/jNoZ+9iCtp41qsk58+6Ys7rr+t1o5cRBMmNqZ/312Md16xZG3ejdBFSV0W7cQWVOPqyBKkN+8Ffv3H6/KNIYFMXHFO4R1a41iMlDQsq3bsIxdSGTEtQYB5twikr+ax4zON7D4kmewV55iVNsEkfU6zpv7KqN+eoqWlwx3KESegKTIKCZDrddtVIuVgs0HTnmcraKK7MVbyFu7+7R/E/8WfDHyRsRutnLo5yXsevNXVEsNi0ingxCUH8qBke53R/Zqx9WFsyjem07V4QIi+3dEfxY2PziZkuIqPnhjufsS/hMRAp0icddjo+jV//jIeP+XcxB2jczWndFkpdoxC1nBDnzw+gre+/rS6hzusK6tmLr9Myoy85k75wDJ81JcQl4AqnLSS09A+sxV/N72WsbNfZWIHp47ODVFZEWhxeRBtJg8iIKtB1hz29vVay/NRvei7yu3MHvIPbW6luQp9ekE9v7f32x88GNHfF4IdAEmRs94nuiBPjmnE/E58gZEHF1QkyQJa2kFswffTXla7qljjqdJWPfWpzwmtGMCoR3PnfZa61ak1qivfoyI6ABeeX8KRpOzg1XNNgRQEJvgdnHUbldJ3ptPh5OkfQPio+gzWmb+4lSXTjiSqhKVk+bWjqqcQmb1vY2wLomM+vXpWqUsNjUierXjgvUfYauoQlIUdCZH6KnPK7ew+b9foFZZHTMbWXL7kpMNOiJ6t/N4/cPLtrHxwY+xn1SUtmD8o1yW9hPG0EDvP9RZis+R1zNlKdksu/oljmzYezQ9TCJ+0gBCOyZQlpKNanZNY6sNkiy7TDNlo57wnm2I7HPudSMv3JlCWcphwrokus3OKSmuctv44US6927G/U+ORnbjqFtfMYq8tXsQHhYlJSQn1cUTadU2gp794tm+MRPLUWcuqSp6m4UWyTWUVKgaRTsPMWfYfVyW+uNZm5538oyuy72XEDWgE3s/+ovKwwWE927H/k9no1lsqGYrkl5B1usY8f0TyDVkwux681cnJ34MTVU59NMSOt7uqVTq34fPkdcjZak5zOx6k7OzFoLMf9aRNW+j20XH2qD4Gej7yq0oASa2P/8tVTlFIEu0umwEgz50L5Z0tmI+UsLCyU9QtOsQsk5Bs9lpNqoXo3592knfo0OXGBbN2YfFTQ9SWYbufZpz3xOjPGaPtLlmLEkf/kVIYS4l4dEumSuaJmjbMcqjnbc/MIzVS1NYNHcv5kobfhs30WznFgxW94uC1QiBvcpC2h8raHPNuJqPPYuIHtjZKfzR/aFp7P2/v8lft8eRfnj7lFOmy5anupcgUCstp5Qn+Lfhc+T1yNZnv/E44vboxGWJwJYxVOUWua1+lPU6uj9+NZ3vuRiA9jdNwFpcjs7fWJ1VcS6xdNrzFG5NRrPZq6sSDy/Zyrp7PmDo5w9VH9etVxxx8cFkppU4NbuQFYlrp/dn5Lh2NaYACgFqlYUOqevZNGiCIwNFlkEIJFli6rRuNbYkk2WJYWPaMGyMI+ZdtKsHCyYeoDLLWnNPPcBeXkXpgaxafBpnL6aoUHo+ee1pnRM1qDMl+zJcith0QX5E9u1wWtfSbHbMR0owhgedk78TX9aKl7GWVmApKgMga/7GGo+V3PSkVAx6Jix9i8lrPyAgIRpdoB+6IH9kvY6Wl47g8oyf6fnkNcevIUkYw87NL2dFZj75a5PQbM6jbNVsJeXHxdhP0HSRZYnHXzyPcZM7EhRixM9PT/8hLXntwwsZPb498ikEsg79spTKzHz8iosIKD8h60WSEAL+/mM3BfnulQbdEda1FZen/sT4BY7GzDWhC/IjpLNnfXPNZq/3hstNke6PXolyUrhJ0iv4RYeRcOGQU54vhEBTVbY8+zXfh1zArwlX8H3IBay9671zTr7XNyKvJULTyFqwiUO/LEPSycRPHEDZocNUpucRNagLET3asPq2t8lfmwQShHRogWKsOU3PGBmCvbzKsdh5tMqux3+vITDBsaB22aEfObJxH9biciL7d/zXLe5U5RQiG3RuRbgAbCXl6EzHy/mNJofi4LTre5/2vY4VXhVHxlIZFOqy4Gkx25g9YxfX/WdAra8pyTJxY3oTN6Y3RzbvZ/vLP5D+1xqnYhlJkTEEB9ByqqtjypiznrV3vENFRh6K0UD7WyfR97Xp1YuK5zrBbZszYdlbrLv7ffLX70FWFBIuGsqg9+92m+JoLSln02Ofk/z9QsdsVsJlkVWoGns/+ovSA5kEJsZSmpxF7LBudLx9Cn4xZyYN0RTwFQTVAqFpLLn0WbIXbj7qdHEsXCoyqBq6QD/USotj1HTC5ykZdB4LIyRF5oqc30mfuYr0WWswRoTQ4T+TfWlVJ2Arq+SnmEvcOnJTVAhXHP7dbeHT6VKRmc/v7a9DM1tJbd+D1A493eaFx8QF8fpHU+t0r6z5G1l929uYc4uoMPlT2H8wxr5d6NK3BcPHtiUg0DECPfTbcpZd8YJzWEaWiBvbh/HzXquTDWcjml11VMh6+HtrqsqsPrdRvCcN4aaPrDskRUaoGrJRj87fyOQ1HzT5Yi1PBUG+EXktSJu56rgTh+NtM47G7uzlVW7PE5pGYKtYR173iUgSY/56EVNECO1vmUT7WybVk+VnN/ogf7o8cBlJ7/7hlKKp+Bvp/fItXnHiAJv/+wWa9ah8rNWCrNrRdK6zqaDAuo+Em4/vx2UpP7BufhJffrkdVdVQd+Wza/cRfv12KwOHJXLxVT1ZddPrrrF1TXB48RbS/15L/vo9ZM3bgLW4nJgR3enxxDUEtzl3tUtqym4ByJq3kbKUw7V24kB17F2z2LBa7ay9813OX/RmnexsLHyOvBYc+Hr+meV62zVMkaGM/uM5dr7xM+VpeTQb1ZMe/73abTMHH670fuFG/GLC2PHKj1TlFRHYMobeL95MmytHe+0eGf+sq56CR2cfIqWzq/6HbLfRWV/mlfupdo1vvt3p1NNUCIEQsGb5ITavS6ebbCIQ1++cUDWWXPy00wJgWWoOab+vZNKa9wnrkugVG8828tcleRxQ1QohyFm+Hc2unvKl0RTxOfJaIM6wK7mkyIR2SiCiZ1tG/vCkl6069zmUXMDyRQeotETS66fX6Tc4AZ3e+z8y2XD8Z2CwWuiyaSm7+45EEgIhSQhJIi5tP0pWIbw0rYYr1Y4De/NrTGSxWFQOdBtIrzXz3O53kSLWBLbyKjY+/EmtO/2ca/jHRaL4Gz3q3NcKSfKos9/UqdPcVJKkyyRJ2i1JkiZJ0jkrY9bm6rHoAk6/J6Fs1NPlgUvrwaJzn9kzdvPyE/NZtiCZ9atS+erjdTz3yFwsZ1hAVRNtrx2HfMLCdEReFoPn/0KH7Wtot2sD/Zf8SdvdG2vVcLm2nMpflETE1KonaDVCkLNsex0sOrtpdcWoOoXaJEWm+Xl9z1oBr7oGGXcBFwMrvGBLk6XV5SOJ7NvBozOX9Tp0ASb8mkWgCzChC/LDGBHMyB+fJLz72aWl0RQ4klfOzJ+2Y7Wq1T0xLWY7h7NKmTdrj9fv1+Opa1F7dOFQ9/4cat+DyoBgdKqd6OxUmqUfwK+qHMXPSJtrvVOw07Zj1CmdtCzXTv/7RJR/STaLO4xhQYz752UMYUGOQrGji9WyUY9s1BParRXB7eNpeckwBn10L4q/sXompvibMEYEn9XFdHUKrQgh9gBNRmu5vpD1OsYveJ1Dvy3n4A+LkHUKkf06kr9xL5XpecQM60bXBy8nICGa4qQ0NKuNsG6tz8pYW1Ngy4YMcOPqbFaV1UtTuPDy7l67l6YJPv90M1sS+mCzqkgIMjr0oFXyDlrs2QY4NNiD2zWn0x0XeuWeer3C9HsH88nbq1z0WQAURaL/kEQGjruT7S/9gDmvCP/mkVTlFKJ5yoIy6Gh7w3iv2He2Eju8O1fm/M7hZduwlVU6Bl6aIGpgZ4xhQU7Hxk8YwL5P/6EsJZvowV1oe/14t/r6QghUiw3FqG/Sfs4r6YeSJC0DHhJC1CqnsKmkH5Zn5LHpkU/J+GctAP7NI1EMekK7JtL1ocuJ7H3uaZacDcyftYdfv9viVskwOjaQNz65yGv3Wjp/P998st4lZq2T4QJ9FgEVpSReMpxWV472ev724awSfvt+G1vWZzhSnjWByaQjONTE069PICjYeQa46fHPSHp/pkscWDLoiOjRhvOX/M9J90QIweElW8leuAl9SCBtrhpDYEtn0S8f7hFCkPTeDLa/9D3WonIMYYH0eOIaOt97caM69DNuLCFJ0iIg1s2u/woh/jp6zDJO4cglSZoOTAdISEjok5bmXhWuobAUljKj041YCktdF48kUPyMDP/mMRIvGd44Bp4FVOUVYSutJLBVrFdji7mHy/jvvX+7iGDp9DKTLurCxVf19Nq9brvyJ6qq3IxyhWDcyBZcc/8or93LE+WlFtYsT+FIfgVt2kfSZ0ALt4u6Qgh2v/07O179CcuREgxhgcSO6EmnO6bQbExvJwejWm0snPg4+Rv2Yi+vQjbokGSZQR/dS7sbzndrR+nBbEr2ZRDcNu6sVGP0Jttf+p4dr/7klK2m8zfS7bErT1tq4Biq1YblSAnGiOAzrsSu1w5BZ+OIfPsrP7D9he89Vg0CGMKCuDL3D1+I5CQqs4+w7OqXyV+XhKxTUPwMDHj3LtpcOea0rlNaXMXGtelYLHa69owjITGset8fP2xl3qw9jhQ9AQajQkRkAM+8MaG6+3xdycku5dE7/nK/Uwg6UMQTf91LSXEVR/IqiIkNIjC4aaSNCk2rcXFv9zu/s/nJL11G74rJwKUpPzg1uLZVVLH0sufIWbYd2ahHs9qIGtiZMTOfb7Lt/OqK0DT2fjyL3e/OwFpYSsyw7vR+8SbCuiSiWqz8GHWx23RGXaCJK/NmojMZSP52Adte+I6KzHyCWsXS+4Wb3A78hKax7YXv2PW/3xyNymWJjrdPoc/Lt5y2b/EVBJ1E9qItNTpxAGG3U7jjoC/EcgKaqjJn+H2Up+UiVA3NYsNeYWb1rf/DLzqMuDG1K49fu+IQX3ywFglQVcHMH7fTd1ACt947BFmWuOTqXnTpEcfS+fupKLfSZ2ALBo9sXaNw1elSVFB5rDjXLcYdu3n/teVs25iJTpFQNcHA4a244faB6HSNK1N0qgyNfZ/Odp+KJ0mk/bGCTndOrd605j9vk7NsO6rZWv2byFuzmxXXvcrYP1/wptlNhlU3vUHq7yuqOzWlz1pD9uLNTFr9/tFFY08DXImKtFzS/17jEMU7+hmX7M1gxfWvYi2poP1NE5zO2Pr8t+x+8zenrlB7PvoL1Wxl4Ht3e+V56pp+eJEkSZnAIGC2JEnzvWJVAxAQH+UQvK8BoYlzUoyqLmQv2ERVfrFLOEqttLDtuW9qdY3ZM3bxyVursFlVrFYVVdWwWlU2r0tnzfKU6uM6do3h9geH8dAzYxg1vr1XnThAfMtQj/nckqaS27wVW9akYrdrmC0qNpvG2qUH+emrxl/fORWq2X0+tVBV7FXHBzC2skpS/1jhMqjRLDay5m/EfKQ2bfPOLkoOZHLo12XO7faEwF5hYfPjn+MXHYrmoUJUs9nRhwSw7flvXV6UaqWFTY9+inZC3YlqsbL7rd9cWvuplRb2fz4Ha2nthdhqok6OXAgxUwgRL4QwCiFihBBNdtk8e9FmFkx8jJndb2Ht3e+ReOnwU6ZrmaJCCa1Ble5cQmgaKb8sZf74R5gz8n72fvK3k7rgMUr2ZaJZ3GdOlOzPPOV9Fvyzl1+/3ep2n8Wismj2vtMzvA4EBhmJaRbkphReo82+zeSHN0M7SZfcrsGyefucqjKbIi0vGuZU6HQMSVGIn9C/+t/mIyUep/eKQU9VblG92dhY5K3a5VZ5FCHIXbUTQ0ggLS8e5uIfZKOellOHYM4v9rjgaa+0UJldUP3vqtwit92RwFGI5klz/XQ5p0MrdrOV/HVJpP6xggNfzat+gxYnpbL341kEt21O+aEcZKPOsahx9PNW/I3IOoVRvz/TpFOOvIUQgqXTXiBr3obqxZ0jm/ax//PZTFz1nlO2RkiHeBSjrlqb5ERC2sef8j6/fbelxmOqKr3Uu7QWJO3IobCgykUgSxIC/7goJFUF2c2io02lvMxCeIR/Q5l62nR/7EoO/bIUS0FZdT9YXYCJ1leOdirjD4iPcu/UcLzcg1o3c/p31oJNHNm0n4D4SBIvHYE+8Ozr92oIC/QYmtIfXRMY8ukD2EorOLxoi0P8zqbSbHQvhnz+ELbSSo8jdqFpTiqlpqhQjxLEmtXuiAx4gXPWkR/8YRFrbn8HJAl7WaXzzqNvyNL9mSj+RlpcMJiEKYOxFpVRtDuVkA4taHP1WJfc03OVnOXbnZw4OKZ+xXvTSf56Ph1vu6B6e9x5fTFFhWKvtDiFVxR/Iz2fub7G+5SXWdzmTVcj4dQQub5ZszwFi5vZhVAUqjp1QmR6mPZqGsEhp1/p25CYokK5cPtn7Hl/Jml/rsYQGkinOy4k8bIRTsfJeh09n76OrU9/5dRWTedvouvD06rbz1mKy5k74j7KDuVgrzSj8zOy/r4PGb/wDaL61ay33tSIn9DffS8Af2N1rYA5vwRLQRmaEEhWO/ogP9rfOgl9gB/6AD+iB3chZ+VOxAla+bJRT8IFg9AHHX/B6/yMtL9lEvu/mOMUilFMBlpeMgxjeLBXnumcbCxxZNM+Vk3/n0Pr+2QnfhJqpYX0mSuJHdGDjrdNYdD799D5rovOeicuhODgD4v4s9d0fmp2KYumPkXhjoNuj02bsdJtb0S10sLBHxY5bZMVhYkr3iF6aDeH/GeACWNkCEM+faDGhU4hBNkZNcdbDXqFCVMbTsa3stxzub/kZyIu6yCy3fkY2W6jbUFqoy921gZTRAi9nr2Bqds+Y+Kyt2l1+Ui3M8wu919Kv7fuwD8uwnFeTBh9Xr2Fnk8dT7Pb8OBHlOzLdGRyaAJ7hRlbaSVzht9HZV5hgz2TN1CMBsbNeQV9SAC6ID8UPyOKn5Hm4/vR9aHLUS1W5gy9hyMb9iKsdjSLDcuRUpZf/RJ565IAGPnTk4R1SUQXYEIf5IfO30hkn/YM+exBhKZRsC2ZrS98y8qbXiegZTSJlwxHNurRB/ujmPS0vHgoQz590GvPdM6MyDW7Sv6GvQDsfvcPtKraT9ElnY7shZtpe+04NFXFWlyOISSw0dIOS5OzsJVXEda1lYsNhTtT2PvRXxRuO0hIxwS6PXYloW40lDc/8QV7PphZPcrO+HstWfM3EhAfRUV6HqboULo+PI3Od1+EbNA7wgtupoDummP4x0Uycelbtc4jV1WNt19cQtLO3Bqf++nXJxAS2nBTdUXxHDYLjAyiR1AF69P3kZXQASFJSJpGwoGdxKXvYfawexkz83lMkSENZm99IUkSHadPpuP0yWiq6vK3FEKQ8uMSt+E0zWLjn/53cunB788qnZLogZ25Ivs3Mmevw3yklOghXQjv1hqAQzNWYi2rdGlurlZZ2f7SD4z7+yVMUaFM2fwJRzbuozQ5i9DOLYno2Za0P1exevpbWE5YJJZNBnQmAxOW/A/ZqCcwIcbr35tzwpFnzl3P8mtecagUCkde7GkhORYedr75Czte/hF7lQVZr6Pz3RfR6/kbGuwLWrwnjSWXPkt5Wi6yIiMpCoM+upfWVzgkW/d/OZe1d76LZnH8oPLX7yH5m/m0u2kCQz57sHq0VZVXRNK7fzhnIgiBZrFRdjAbgMqsI2x+4nMq0nJpffUY9n4yy2UVXhdgov3NEz3a6xcdhl90mMf9x/ju0w3s3HrY435Jgun3DaFF4qmv5U2sNWhX2+0a4/95icDb3iblt5+xynoKo+PIatOFrFadCCnOp+SiF7lq5RsNaHH94/a7LoRLu70TqcorJnPuBiqzjpD0/kysxeXEjelNr2euI6h1HMnfLWTrs19TkZ5HQHwUPZ+9nnbXN35ehM7PSOKlI1y2l+zLcC+JKwTFSanV/5Qkiaj+HYk62sovd81ull/zssvvSDNbsZqtrL3zXS7c8qlXn+EYZ70jLz2YzZLLnquTfKVQNUr2pbPr9V+r04Q0i43d7/6BrbySge96J9ezJuyVZuaMuA9LQRkIUd1oeNUtbxKQEENo55asu+u9aid+IsnfzidmaNfqir28NbtrbJF2DLXSwt6PZ9H9iavofPfFJL0/w3GOJtAFmIgb25vEy12/6KdDVZWNpQsOuN8pQZ/+LZh+/xBMpprb4tUHiW3C2b39sIsUgE4v06pdBPogf0Z8/wSZc9aTEteRrFYdqxtO5BtbsEiLo/vi3XQd06XBba8JIYRXF+klWSZqQCdHG0M3aFYbW5/+mtIDmdUzwIPfL+Tg94vQ+RuxV1mq16XK03JZfeubVGYX0OPxq7xmozeRFNn9DFWSCO2c6PG87S98V6MfKt6TTuXhAvybRXjJ0uM0/UDfKdj78V8eV5DdISlytaqcbNCh+BkZ+sXD7H7rd/e5np/NwVpS7lWb3ZH6+9Fc3pO+PGqlhR2v/Ej2As+5y8Kuseut36r/bQgN9FzPcBKyUU/RjhT6vnILE5e/Q+d7LqbDbRcw5s8XGD3j+TOejZSXWcjJKmXLunTPtgho1ym6UZw4wOjx7dG5CZ/pdAqjxzuKwIRdpdwiyGzd2blrkCyjKjr+mLG3ocytESEES+bu496bfueGi77n/pv/YMWiZK9df+B7d3usu1BMBop3pzo3XxEczc02u/bNtGuOxVUPue6NSfGeNHa8/KPblEHZpKfHf6/2fO4Jo3V3SJJUb02fz/4R+YEsp5XjmpB0CgLh6KiuUxCaYOCHdxE7rJvHl4Fs0FGWcpiIXu08XlezqxRsOYC1vJK8lTs5vHQbAfFRdL7n4upp1ymf42A29nL3XYiKk1LJXrylxi+BOe94TC5mWDd0/kZsp1joBUeBg1+MI6QR2ac9kX3qVsVaWWHls3fXsH1zFpqm1dhAAaBLD3cyPg1DaLg/T7x0Hp++u5qc7FIAYpsFM/2+IYSGOzIPZL0OS7u2yJqKevJLTZJIzz2DzlH1wD+/72LW7zurs4IKCyr57rMNVFVaGT+l7gvIkX3aM+ij+1h757vOpbCS5Bj962Q4Dal4oWpkzFpLq8tH1tm2E6k8XEDytwuoSM8jekhXEi8ZdlpFfdte+M79TFaCbg9NI7RTAgXbkgmIj3KJcwe3b0FFRr7Ha/vHRXgt3fBkznpHHj24C9mLNqOetLgpHRUdUgx6hKYh7Jojn9OuoXH8i7j+3g+IP+9rj9dXLTaK96Sz9+NZKP4m2l43zqlkP33WGlbe+Dqa1eY8IpEk0v5cRf//3U7H/1zg5srOhHVJRBfo5xqbkxzT0eTvFyJqcOQxQ49P72VFYezsl5k/7mE0u4pmtSNUzZEueGJzaEUmpH18jdPF0+Xtl5ZycF8+qlq7KUFcfOMuFrZsHc5L715ASbHjc3e32Nrp+rFsX+K+cMNoavyfkNWq8vcfu1xSO60WlZk/72DMxI5eybLpOH0ywq6y6dFPkRQFoar4xYbTYfoktr3w/WlfrzL7SJ1tOpGsBZtYcvHTaEelI5K/W8jWZ75m8toPar24mLc2yWWREwBJJnvhJna98QuyQYdmsdHiwsEM+/IRh/450POpa8lbu9tteEXxMzL0q0fqrS7lrA+ttL9louODPHHaJ0noA/24OOlrBn5wD/1e/w8hnRPcO0IhSPtrDe1vnoDi5yyIJJsMGEICWHPbW+z/fA57PpjJnGH3sfXZrwEo2p3KsqtexFpU5trTUwjUSgsb7v+oVqGZhKlDMIYFuua3CkATNWbhKP5Gej13o9O2yN7tmZb5K0M+fZC+r9zKeQtep/mE/ih+BvRB/ugC/Qhu15yxs146pW21JTO9mNTkglo7cUkC6RQyCfVBRVY+y65+ke+CJvFd8GRW3vAaBqvZY8bM6HsmYPB3FcvS62WGj21b3+aekvzcspNrmqrRVI2iAu+UgQN0uuNCrsz9g3GzX2bSmve5ZP+3dLz9QrcZTzUhKTKhXuwvqlptLJ32PPZKS/U6kr28ior0PDY+9EmtrxPQPNLtdlmRObLlAKrZiq20EtViI2PWWlbc8Fr1MbHDuzP0i4cxRgaj+BuRFBldoB+trx7D1O2fETvMezr6J9P4w4k6YooIYfLaD1hz29vkLHe0uooZ3p3Bn9xPcJs4glo34/DSbVSk57k9XzXbsBaV0e/N2yhPy3U04j36pdQHmLCWVR53oppArbKw841faXX5SJLe/cPt4uOJyAYdh5dso+VFQ2tchFIMeiatfp+VN7xG7qqdIEnIOsVz02dZQpJlYoZ1Y8Bbd7htuqvzM9J62nEZ1riRPSnZl0Hh9oMEJEQTNaCT10YIqQcL+PPn7ai1bIcmydC7fwKKh6rC+sJSVMbffW/HfKSkuqDp4I+LObxsGxft/tJJz/sYOp3Mwy+O541nF6NpArtdQ6eTadk6nIuv7NGg9rsjOMSE3e7+c1c1QWCQdxUbdf4mYoZ0rf63PtCPMTOfZ/FFTzsaMdQi8cCvWTjNRvfymk05y7a7fZloNjuHflvOsK8frdV1uj1yBcuvetGlrsJd1o5qtpLx91qqcgvxi3GoSbaeNorES4dTeiALfZAfAc3rJ5RyMme9IwcIbtuc8xe9iXo0z1UxOBalVKuNhZOeIH/9Ho8dtnUBRmJH9KAs5TCHl251+jJYCkvdLtRpNjspvy6jZF+Gq5a5G8rScpg97F7y1u5GMRpoc81Y+r3xHxeJ0ID4KM5f9CbWknJUs5Ullz9P3sqdbq/Z9tpxDPuqdl/OEwnp0IIQN3nndeG377ay4O89tdYfMZp0BAUbuf62/qc+2Ms4hIoqnf5uwq5iKSjl4PeLPIbB2rSP4t0vL2Hz+gxKisy06RBJu45RTULCISjYRNeecezamu3k0HV6md4DWnhN9rcm4sb2YVrWr6TNXEX2ws2k/rECWa9zKGTa7AhNc2iXaILgds0Z8+cLXk3rPSZD4I6aQpInkzBlMN3/ew3bnv8WWa9DkhyLudaSCrfdmRSTgfK0vGpHDo7QZmjHhNN7gDpyTjjyYxxz4MfY88GfHmNW4IhbRfbrSMywbiy/5iWXOLunbAuhamhWuyMla/0ej+23ADSbypYTdKHVKgvJ3y7gyKZ9TNn4sVvNB0NIIIRAi0kDKdi0z8UuXaAfzc/r5/GeDUlaSuFpOfGxkzrQvlO0x+YJ9Y1jPcX1+2CvMHN48ZYa1zOMJj2DR7SuT/POmP/cN4R3Xl7KoQMFKDoZ1a7RrlMUN985qMFsMAQH0O768bS7fjwD37uL9FlrUM024if0xxgeRMG2ZEyRIYR28r4QXeyIHu4TFiSJuLG1k1YGyF6ylb0f/YWkyGg2G8bIUEb98jQLJz7m9neumq0EtWnm5koNyznlyE9m/+ceNJkBY3gQXR+8nC4PXoYkSeStTqrV6BpA8TPQ8qKh+DeLYN///eP2DywbdEiKQnjvthxZ69wwWLPYKD2QRfbiLTQf56IRX02H6ZPZ88FMzLnF1VM72agnMDGGlpcMq5Wt9c2a5SnYapn+2a5TFNfe2vCj8GMU7U7lyCb36oqSTiGgRXQDW+Q9/AMMPPHSeLIzSsg5XEqz5sE0a954C8nG8GCXTkT1GSM2BAfQ743/sPGR/3MMfISoTi/u//YdtbpGyYFMFl/4pFM4syrrCAsnPEanu6aS9O4fTiEXxc9I6ytHYYpo/Ores36xsybsHhYIZYOO7o9fRffHr6oexR/TmXBBkpzkQHUBJlpdNpKofh0JiI9i4op3iOzf0ZGbrsgEd2xBwkVD6PrwNC7a9QVVmUfcroKrVRaObKxZstUYGsiUTZ/Q/tZJmGLC8I+LoMt9lzB59fsus4/Gwm47dYqhosj4B+i55e7BDWOUGyzF5cwZfi/WYvcLz7JeR4fpkxvYKu8T1yKE3v1bNKoTbyw63XEh4+e9RsLUIUT0aU/ney7mol1f1LptXdJ7M1DdrHlpdju6ID+6P34V+iB/FH+HNkv7WyYy+OP7vf0YZ8Q5PSJvOXUIez+a5bJQISkKzc93Hhl2fehyVl7/qsvioi7QRO/nbyT9r9WOkvVbJpEw5bhDCu/RhgvWfYitvApJlqpTkY7h3zyS8jRXjRHFz4i/hxXyE/GLDmPQB/cw6IN7TnlsY9B3UAIrlxzEYnb+jPV6mfiWoeh0Ch26xDBuckdCwxpP8vTgtwscP1J3Lx1FZugXD3l97cBHwxMztBsxQ7ud0blFOw+5jaerVVaKd6cy4rsn6PrwNMx5xRgjgquVIZsCdXLkkiS9AVwAWIGDwI1CiGIv2OUVuj9+FYd+XYalsKw6u8SdJjNA4sXDKNqZws7XfnaMwIUjjDHu75eIGtCJLvdeUuO9POkyd314GiuuftmlalRS5HOisXPHrjG06xjFnp051WmHeoNCs+bBPPHSeAxe7upzphTuSPEYZovs3a5az8bHv5eIXu0ceeQnDfwUPyPhPR1ppopBX29FPXWhrr+yhcDjQgi7JEmvAY8Dp59KUU/4RYcxdfvn7H53Bhmz1mAIDaDTnVNdNJmP0euZ6+l011RyV+3CEOxPzLDudVZAbHnhELr/92q2v/Bd9QtC8TMw9u+XzkpR/pPZvf0w+5Jy0U4oaRaaxvX/GdBknDhAWLdWKP5GF2cu6RTCa6jarQ3ps9aw8dH/o3R/FsaIYLrcfyndHpnWpNQAVVVj++Ys0g8VERkVQL/BCRgbSRqhqdL53ovZ/8Vs7Cc6cklCMeppf+P5nk9sAkieulec9oUk6SLgUiGEZzGCo/Tt21ds2tT0+x56E0tRGXmrd6EPDiB6SJcm9SM/UzRNcO9Nv1Na7JrrntAqjBfebjoxZ0tRGb+3ucYlRq4LMDFl0ydnHFbZ/eMyfntlDgVhMRjMlcSn7CHCXEzba89j8Mf3ecHyulNWaubFx+ZTXFSJucqO0aRDUWQef3EcCa3CT32BfxF565JYecNrVKTnIYQgtFNLhn/3uNs6jcZAkqTNQgiXDAlvOvK/gV+EEG5rdSVJmg5MB0hISOiTlpbmlfv6aDzSU4t48bF5LvFxAEUn895Xl3q9GKUuFO06xPKrXqI0OQtkCVNkCMO+eoRmo86sMKUgv4LHbvwJm6QgFB0IDVlVabV3K4lZB7gs9Uen/OLG4v3Xl7N1fYZLxW1EVAD/+/SiJpEL39SozD6CpFNqJdPckHhy5Kec+0qStAhwp2z0XyHEX0eP+S9gB37wdB0hxKfAp+AYkdfSbh9NGPkU5fVNzUGEdW3F1B2fU5GZj2a1EdiqWZ1s/OXrTVhlPRyrBZBkNJ3MoU69iS85TOH2FJqf17iO3G7X2Loh061sQnmZhfRDRbRs3fgvm6aGf9ypExGaEqd05EKIsTXtlyTpemAyMEZ4a3jv46ygeYsQAgINLiNySYLE1uEEBNZ/ReGZ4K3Fqu2bs4878ROQNI0jwZGeU1obEE3VEJ66uMsSVVWnIVnoo8lSpzxySZLOx7G4OUUIcWrNVB/nFJIkccdDwzCadOj0jq+SwaDgH2DglnsaL2e8oZBr0IkJjA0jrGurBrTGPQajjuYJ7nPKbVaVyOhAj13efZw91DWt4APACCw8OkVdJ4S4rc5W+ThraNcxmtc+upDlCw6QnVlCqzYRDBvbtknExrWjlbo1Ody6MHBYIssXHUC1n+QIFYVp3zadvP9Bw1uRkbrVZbvdrvHIbTMJDDZy9c39GDA0seGN8+EV6uTIhRCNr+Hpo9EJC/dn6hWNrwJ4jNzDZXz7f+vZvSMHCejasxnX/ac/UTFBXr3Ppdf0JGlnDkUFlVjMdhQZZFlm+gPDiGgC+hvH2LIh0+M+VRWUFJn55K1VGE06evaNb0DLfHgLr2WtnA7/xvRDHw1DeamFR+/8k4pya7V0gCRLBAYaeO2jCwkI9O5MwWZT2bQmnaSdOYSF+zFsTFuiYgK9eo+68sCtMyjIP7UmeUioife+vqwBLPJxpnjKWjmntVZ8/PtYumA/FovqpP8iNIHFbPdqD8tj6PUKUbEOx304s4TkffketcEbi9btIz02njiRkmJzk7PdR+1oOqV3PnzUAZtNZf2qVBb8vQebG0ldq1VlX1IeE6Z6t+P9Hz9sZd4sxz2FgO1bspk/aw9PvHRek6lsnXp5N7ZvynRpBeeOslIzYUf7lfo4e2ga3zQfPuqAxWLnxcfmkZtdhsXiXhteUSRimnk3Rp6dWcLcv5xfHBaznYy0It57dRmHs0qx2TR69Yvnoiu6Vzd0bmjiW4bx2Avj+P6zjaQcKKjx2KawSO3j9PGFVnyc9Syas4/DWaUenTg4Kk1Hn9/e4/4zYfO69OrMmBOx2zR2bj3MkbwKSoqqWLk4mafun01psfsuVQ1Bm/ZRPPPGRJ59c6LHMIvBqKBvhGYfPuqOz5H7OG3SUgpZvyqV9NSixjYFgDVLU9yGU8DR7szPX8+dDw0nplmwd28sPDaRckJVBZWVVub/vefUB9czrdpG0CLRtexclh1ZOD7OTnyhFR+1pqLcwpvPLSYzvRhZltA0QctW4dz/5OjGreL0MMKUZRgysjXXTe9fL23leg1owV+/7URTTx17tts0tm3K4rJra992rL54+NmxfPD6cg7uP4KiyKiqxoQLO3Pe5E6NbZqPM8TnyH3Umk/eXk36oSKnzIZDyQV8/t4a7n1iZKPZNXBYIjMytjtJ6QIoOoXzL+xcb71B4xNCGTuxA0vm7sditTskihXJra4JNJ34c3CIiSdeGk9BfgXFRVXEtQjBz88naXs243PkPmpFWamZpB2HXdLT7HaNHVuyqCi3eD1HuzZYLXY2rEpzKTOXZDhvckfi4uu35dkVN/ShR5/mLF+YTGWllc7dYvn9h20uoR6jUce4yR3r1ZbTJSIqgIiogMY2w4cX8DlyH7WirNSCTpGx21wX9xSdTHmZtVEc+aqlB8k5XOrSN1SnU+jas2GqKzt1i6VTt+MCoaHh/nz+/prq8JPQBP0Gt6RX3+YNYo+Pfx8+R+6jmvIyC3P/TGLT2nT0BoVR49sx8rx2KIrsqFb0EItWFKnRRnZrV6S6zY+2WVU2rk2nc/eGL5UfOCyRrj2asWZ5CssWHCDncBkb1qSyfXMmV93cl8EjWje4TT7ObXxZKz4Ax0LmU/f/w7xZSeRkl5KRWsTPX2/mnZeWIoRAr1e4+MoeGIzO8WaDUeGSq3qi0zXOV8lTupwkOZQYG4uAIAPLFyWTk12GatewWlTKSi189dE6dmzJajS7fJyb+By5DwAW/rOXshKzU+jEanFUQ+7dlQvA+CmdueG2AUTFBKIoEtGxQdx050DGTmq82O+IcW0xmlwnlnqDwqDhjScjuy8pj/zcctST8sytFpUZP21vJKucKS+zsGV9Bru3u659+Di78IVWfACwaV0GNjfxb4vZzo4tWdUx4CGj2jBkVJuGNs8j/Qa3ZOPqNHZsycZitSPhGKWfN7kjiW0ar7HD4cwSjw0dcrJKG9gaV/75Yxd//rwD5ehMSlEk7n18JO06RWOx2DEadafsAOWj6eBz5D4A8PNz/1VQdDJ+/k2z0w84utzc+chw9u3OY9PaNHR6x0i8sduXxTQL8ugIo2MbVx1x++YsZv26A5tNxWY7vr7w+jMLMRj1mKusKDqF9p2imTC1M116NPM59SZOnRy5JEkvABcCGpAH3CCEyPaGYT4altETOpCWUuRS5i7LEoOGJzaOUbVEkiQ6do2hY9eYxjalmo5dYwkN9ycvp8wpv91gVJg6zaHdbjHb2L8nH51Opl2n6AZbZ5j7ZxIWNwvEdrvAbrcCoFlVdm8/zN7ducQnhPL4i+Oa9Av9305dvzlvCCG6CyF6Av8AT9fdJB+NwYChifQdlIDBqCDLEjq9jF6vcPXNfb3ekOHfgCxLPP7iONp1ikKvlzGZdPj567nqpr70HtCC5QsPcNf1v/HhGyt45+Vl3H39b+zefrhBbCsqOLU2+TFUu0ZWejE/f725Hi3yUVe81lhCkqTHgQQhxO2nOtbXWKLpkpZSyI7NWeiNCv0GtWyyBSNCCFJ+XMyu//2KOa+YmGHd6fnMdYR2TGhs01woLqykvNxKbLMgdHqF5L35vPbMQpe0SYNR4Y2Pp9a7SuIXH6w9bW12RSfz+a9X+UIsjUy9NZaQJOklSZIygKvxjcjPatIPFbJycTJZmSWEhfsTEubX2CYhhHCrZbLhoY9Zc9vbFG47SGV2Aam/Lefv/ndQuDOlEaysmdBwf+ITQqulAuZ50EwXmmDV0oP1bs+AYS1P+xzVrvHF+2vqwRof3uCUjlySpEWSJO1y89+FAEKI/wohWgA/AHfVcJ3pkiRtkiRpU35+vveewIdX+PqTdTzz4BwWzt7H2uWH+OL9tTz/yNwapWHrE0txOStvfJ1v/SfwjWE8f/e/g/z1DvXAiqx89n38N/YKc/XxQtOwl1ex8aFPGsXe0+FIbrlLJSqAzaaRn1v7sMeZIjTcpmyeivWr08jOKKkHi3zUlVP+NYUQY2t5rR+B2cAzHq7zKfApOEIrtTXQR/3z7f+tZ+m8A07bLBY72RnFzJ25m/iWYaxaehBNFQwe2Yp+g1ui1FNnenA45bmj7qdkTwaa1QbAkU37mDfmISav+4CiXYeQ9AqYXc/NXbWz3uzyFh26RJORWuSSu2006WjfKbre79+8RQiaB2GvmhBCsGt7NnEt6le/xsfpU6dfoyRJ7U745xRgb93M8dHQ5B4uY+mCA2732Wwas2fu5tN3V7N1QybbN2fx5Yfr+N/zi10KXbxJ9qItlB08XO3Ej2E3W9j24vcYQgKQPHRH0AU0fjjoVIyf0hm9QXGSPFAUiaAgI/2GnH7Y43QJjwyg94AWDhtOwGBUuOqmvh4liRVZxuRTSWyS1HVY9erRMMsO4DzgXi/Y5KMB2b4ps8buCFaLisV8PLxiMdtJ3neETWvTvW5LXk4ZS+fvZ8W8vZRKBjJad+Zgp94URDdHIIEmyF+3h2ZjeiMpruX3islA+1smet0ubxMe4c/Tr02gS/dmSLKEopPpO6glT78xocFkBabfO5iR49phMCrodDLBISauvqUf46d08phuKoSgz4AWDWKfj9OjTnnkQohLvGWIj8ZBVmRkRXLR8q4Ji9nOmuWHGDA00Ss2CCH46ctNLJl/AEkC1SZQR05FEhpCVshqZcO/vISeq+cR0CIKxaBn7N8vsXDiYwhNoFlsyAYdEX070PPp67xiU30T1yKER54bWy2/62mGUV/o9ArX3NqPK27sg8Vsw8/fgCxLlJdZWLXE/YLxlMu6NYrCpY9T46vs/JfTu388P391+jnC3kxD27w+g2ULkp0zOSQJITlGp5pOT0VQKOld+3D+oxcAEDO4C9OyfiVtxirMeUVEDepC9KDODe4Q60pj26vTyehOcM5rVxzy+FI/uP9IQ5nl4zTxOfJ/OeGRAVx2bU9+/34bNpvqNpviZIwmHUNHnVqKde3yQ8z6bSeFBRXEtQjl0qt70qWHq6zswn/2njI7Rig68tt0osXkQdXb9AF+tL123KkNPouoyMynYOsB/JtHEtGrXYM7+vzccqwe+p/m55Y3qC0+ao/Pkftg/JTOdO7ejBWLklk8d5/HVmXgcOLdesXRPCGUmT9vp6LMQtdecXTvFYd8QibLP3/s4q9fd1QXvaTsP8I7Ly3ltgeG0megc9FOWamb9BM3CP25+3XV7Cqrb3mTQ78sRTYaEKpKYMsYxs19lcAW3stkKcivYO+uXPwC9HTrFeciA9ymfSRGk85pXQQcM7C2HaO8ZocP73Lu/jJ8nBYtEsO4+pZ+LJqzz+MxYRF+3HzXYIoKKnjyvn/QVIGqaqxYfJCExDAeeX4cBoOCucrGX7/scBnZWa0qP3yxid4DWjiNNHv0iSc3u6xGKVVJlujW+9ztsLP95R849PtyVIsN1eLI1inZl8GC8x/jol1fOH1eu7cf5vfvt5KdUUJYhD9TLu92ymYVdrvKB6+tYMeWLBSdjCzLSBL0HZjAwQNHMOgVRo5vx+ARrQgKNmK3qU4vdL1BYeJFXern4X3UGZ8euQ8nwiI8p+9dfXM/ElqF8e3/bcRmVatTEC1mO6kphSz4OwmAzLTiannUkykuqqKywuq07fwLO+HnX3Nam5+fnsuv7XU6j3JWkfTeDNRKi9M2oWpUpOdSsOV4eujmdem889JSUg78f3t3Hh3VdR9w/Pt7b2a0b0hYQgsCyiqDxGbAayAW2IAKNj6Q4mNO0tZJ47rYPgnhBNM6ro+zuPSYpMWxy3FJcEJOY5KmdsKpwdjY8RJ2RDg2RsZYICMWgSQQ2kYzc/vHCIGYGSTXI82i3+cvNPP03m/uiN/cuff37j1PW5uHUycv8rOf7uT3m7vXzzc2tFJ3pgljDMeP1fPwss0c2PMZXq/B3e6lrbWD1pYO3nnzE2prLlB9rJ5fbdjLvz/zR554Zi6Tphdh24IIjByTw+of3EXuEF1zJ1ppj1x1s3BJGS+t342no3tvelBOMlNvHspb2z5GguToDreXt7d/QsV9E0hNT8AbonctAq6E7n92GZlJPLJqJj9cvS3oRJsIPPEvc7khLz4TiTEGd0NT0OfEtmmpPQdTRmOMYdOLewO/6bR7eXXzIWZXjOVCYxsvPPsOJ6obsCwhOdlJW5s/cffE3e6l6vBZTtY0snzll/B5ffgMEdv9SfWevkOqmzvK/4L5i0pwOi0SEh04XRbFI7L4px/djYj4J0RDVDV4OhNMXn46QwrTAypbHA6Lm24uDro9m8tlByT4y2yHRXJK/N6IIiJkjA5en+1zdzBo4kgAWprdNDa2Bj3Odlh8cqSOp7/7Gp8ePY+nw7+9XGNDW6+S+GXtbR4q9/q3orNsS5N4jNAeuepGRFi0dCJzF5ZQU91IekYieQXpXc+XTirg5ZcOBPye/6aWK5OYj66axQ//cRtNF9rwGYMgFBZnUnHfjfzyxT1UfXCGQTkpLPzKBIaPzKFgaGbImDIykkjPSAzr64w2N/3rN9mx5Cm8rVeGV+zkBIYtur1rstOV4CBUEUtrSwdrnnoDQXpVeRSKbQuJITYZUdErbMvYfh66jG1s2/Sfe3h729GukkGn0yI1LYGn1lZ0S7g+n+HwodOcO3uJwuIsbFv4weNbAzY1KJ8/hmVfn8aOrVX8asPebsu7ulw2D6+8g4lTC/vnxUVQzZad7PnOC1w4UoMrM42SR+6lbPUDWI4r32D+Y+277H7veJ/tsel02Ty9toLGhlZefmk/NccbSEtPZN49Jdw5b0zE694HulDL2OpHrwrp4oU2Dh2oxbKE0skFXWtwzLprFNVH6/nk4zosy2L8xCE8uPxmUtO695otS7rVjT/xrS1Bd6bZvuUIZVMKmXXXaLIHp/DKy4eoO91EYXEm9/xVWb8sJBUNiubPoGj+DIwxQRNm7WcXGD4qm2Mfn6f+XDMdHT7C2RFzOC2WLJvE+XPN/Pj7O7rG4s/XNfPrl/ZzqraJZV+/KWzXU+GjiVwFtfXVD9n8i0pshz+heL2Grz00neEjszuXt/WPlXvx8uHBU/x2UyVf/eaMrt+/0NjK7veO4273MH5iPrlD0jhRXR/yept/sZ/SyfmUTi6gNI7LDHvj2iTu8/pY/5P32bvzBCL+D0ixBKfTCnnzztWcTov8okyOHwvd/lmDkhgxKgePx8fPfvqnoBOqb22rYsHi8WRkRv/CZAONJnIV4OiROn6zqbJzc94rj298fhcjRmfT1ubpttBWe7uXd944xrx7xzM4N5X33z7Ghud2AuD1+vifX/+ZiVMKrrs4V/35lj56NbHvjdeq2LfrRMBmFD2Ncoj4b+D6u8du483Xqq5zIDTUt7JvVw37dtWEPMzptPn06PkBMcwVa3RKWgXYvuVI0B1sPF4fHx+uC5qQLVs48sEZGupb2PDcTjrcXjrcXnyddcuV+06SdZ0tzIYOywrnS4grr//ho4Bt4cC/8NXlb0zBGOOfp3jvrWPkFaRj2yGO7eXojM9n4n7SOVZpj1wFaGxoCVr54PManE4bb5Ct10QgOcXF7nePB00M7nYvSbkOpEECyhcdTot7vlIarvDjTvMld8jnJk4t5ND+WmyHRXubJ6AO393uDcuSwyL+ev/hI7O/8LlU+GmPXAWYMLkg6LrYCYkOJkzOD9iQAPy9tTOnLnK69iIeT/BxW6/H8My6BQwpTEfEX+qWnpnIQ9++nTE35ob9dcSLseNzgw6jCLBoaRn/tnExjz0+K+SGENdyOC0SEx0h7769mm1bJCY5yMpOZsX3vqxVK1FKe+QqwMzZo9j26mE83rauLcFsh0VmVhLfeOQW1q35I1WHz+LzGsQSOtz+VRN/s6kS8E/WXVtN4XBYTJpeRG5+Oj9at5BLTe20t3nIyk7Wndl7cN/9Ezl0oBZ3u6frm5IrwaZ0cgGFxVm43V7efK2Kpovt1z9RJ4fDYuWT5Zw53cTPn98VsEDWZS6XzR2zRzJlehFjx+fp+xTFwtIjF5EVImJEJCcc51ORlZLq4p+fnc+tM0eQnOIiNS2BWXNG8b01c0lKcfGdJ8tZ9fQcFt1fxuXM0uH24unw4eksibt6PNZ2WCSnupi3sKTrsdS0BLIHp2hy6IX8ogyeXDOPydOKSEl1kXNDCouWlvH3K24HYOPzO9m/O/Qk5bV8XkNWdjLTbxtG4eAEbAJr0m3bIis7maV/PZWS0iH6PkW5L9wjF5EiYDYQ/r2/VMRkZiXx4PJbeHB58OdHjMrh3NlmbIdFR0f3RGCMfxhmUE4K7W0eJk8vYv6iG0nXsrX/t/yiDB5ZNTPg8ZZmN7verQ54D67HZwzJKS4+Wvc7hv58A67MIZwcNoaWtCw8The2w2LarUN54MFpeot+jAjH0MpaYCXwShjOpWJIS7P7OlvECd//yV/2azwDUWNDa9APU7hSnnj1KJfD6V/vRlpb2bfqRUybm9zmT8k9+an/AMuiaMEtlH/rgX6IXoXLF/q4FZEFwEljzMFeHPsNEdkrInvr6uq+yGVVlBh7Yy4mSEdQBMZNyOv/gAag7MEpXfMY13I6bYpHDMKVYJOU7MTlshk97ga+9tB0Tu2oxAq2UYfPR+3W3X0ctQq3HnvkIrIdCPa/cjXwODCnNxcyxqwH1oN/rZXPEaOKUnkF6Uy/rZjd7x/vqnMWS0hIsFm8bGJkgxsgEhIclFeMZfuW7rXmrgSbORXjWLxsEieqGzhTe5GCoZnkF2YAYLkc/rKXICyH1kDEmh7fMWNMebDHRWQCMBw42FmSVAjsF5FpxpjTYY1SRa2/XX4LI8cOZtvvP+LSpXZKJuRx79Iy8vLTe/5lFRaLH5iE02mz9ZUP8XoNtsNi7sJxLFjir80fOiwr4Iar/PIpQZcjtlwOhi+d1S9xq/AJ2+qHIlINTDXG9LjVtq5+qFT4eTw+WprdpKS6sO2eR01rtuxkx5KnMD4fvvYOHKlJJOdnU/GndSRkxecmHrFOVz9UKs45HNbnuoW+aP4M7juykaMbt9J88hx5d5RSvOh2bFf8buIRr8KWyI0xw8J1LqVU/0gpHEzZaq1QiXVaJKqUUjFOE7lSSsU4TeRKKRXjNJErpVSM00SulFIxLmx15J/roiJ1wPE+On0O0GMt+wCnbdQ72k490zbqnXC1U7ExZvC1D0YkkfclEdkbrGBeXaFt1DvaTj3TNuqdvm4nHVpRSqkYp4lcKaViXDwm8vWRDiAGaBv1jrZTz7SNeqdP2ynuxsiVUmqgicceuVJKDSiayJVSKsbFdSIXkRUiYkQkJ9KxRBsRWSMiH4nIn0XkdyKSGemYooWI3C0iR0TkqIh8N9LxRCMRKRKRHSJyWEQ+EJFHIx1TtBIRW0QOiMgf+uoacZvIRaQImA2ciHQsUep1YLwxphSoAlZFOJ6oICI28BwwFygBlopISWSjikoe4NvGmHHADOBhbaeQHgUO9+UF4jaRA2uBlYDO5gZhjNlmjPF0/rgT/1Z9CqYBR40xx4wxbuC/gIURjinqGGNOGWP2d/67CX+iKohsVNFHRAqB+cCLfXmduEzkIrIAOGmMORjpWGLE3wD/G+kgokQBUHPVz5+hCeq6RGQYMAnYFeFQotGP8XcofX15kZjd6k1EtgN5QZ5aDTwOzOnfiKLP9drIGPNK5zGr8X9N3tSfsUWxYHvL67e6EEQkFfgt8Jgx5mKk44kmIlIBnDXG7BORmX15rZhN5MaY8mCPi8gEYDhwUETAP2SwX0SmGWNO92OIEReqjS4Tka8CFcCdRm8ouOwzoOiqnwuB2gjFEtVExIk/iW8yxvx3pOOJQrcCC0RkHpAIpIvIL40xYd9bL+5vCBKRamCqMUZXaLuKiNwNPAt8yRhTF+l4ooWIOPBP/t4JnAT2APcbYz6IaGBRRvy9pI1AvTHmsQiHE/U6e+QrjDEVfXH+uBwjV72yDkgDXheRShF5IdIBRYPOCeB/ALbin8B7WZN4ULcCy4Avd/79VHb2PFUExH2PXCml4p32yJVSKsZpIldKqRiniVwppWKcJnKllIpxmsiVUirGaSJXSqkYp4lcKaVi3P8BfJoVtKQUobkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:, 0], x[:, 1], c=y.reshape(-1), s=40, cmap=plt.cm.Spectral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以先尝试用 logistic 回归来解决这个问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.from_numpy(x).float()\n",
    "y = torch.from_numpy(y).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = nn.Parameter(torch.randn(2, 1))\n",
    "b = nn.Parameter(torch.zeros(1))\n",
    "\n",
    "optimizer = torch.optim.SGD([w, b], 1e-1)\n",
    "\n",
    "def logistic_regression(x):\n",
    "    return torch.mm(x, w) + b\n",
    "\n",
    "criterion = nn.BCEWithLogitsLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以广泛使用的模式total_loss += loss.data[0]为例。Python0.4.0之前，loss是一个封装了(1,)张量的Variable，但Python0.4.0的loss现在是一个零维的标量。对标量进行 索引是没有意义的（似乎会报 invalid index to scalar variable 的错误）。使用loss.item可以从标量中获取Python数字。所以将索引去掉或者变成.item()。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 20, loss: 0.6747000813484192\n",
      "epoch: 40, loss: 0.6731889247894287\n",
      "epoch: 60, loss: 0.6731489300727844\n",
      "epoch: 80, loss: 0.6731469035148621\n",
      "epoch: 100, loss: 0.6731463670730591\n"
     ]
    }
   ],
   "source": [
    "for e in range(100):\n",
    "    out = logistic_regression(Variable(x))\n",
    "    loss = criterion(out, Variable(y))\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if (e + 1) % 20 == 0:\n",
    "        print('epoch: {}, loss: {}'.format(e+1, loss.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_logistic(x):\n",
    "    x = Variable(torch.from_numpy(x).float())\n",
    "    out = F.sigmoid(logistic_regression(x))\n",
    "    out = (out > 0.5) * 1\n",
    "    return out.data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\envs\\mytorch\\lib\\site-packages\\torch\\nn\\functional.py:1806: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'logistic regression')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(lambda x: plot_logistic(x), x.numpy(), y.numpy())\n",
    "plt.title('logistic regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，logistic 回归并不能很好的区分开这个复杂的数据集，如果你还记得前面的内容，你就知道 logistic 回归是一个线性分类器，这个时候就该我们的神经网络登场了！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义两层神经网络的参数\n",
    "w1 = nn.Parameter(torch.randn(2, 4) * 0.01) # 隐藏层神经元个数 2\n",
    "b1 = nn.Parameter(torch.zeros(4))\n",
    "\n",
    "w2 = nn.Parameter(torch.randn(4, 1) * 0.01)\n",
    "b2 = nn.Parameter(torch.zeros(1))\n",
    "\n",
    "# 定义模型\n",
    "def two_network(x):\n",
    "    x1 = torch.mm(x, w1) + b1\n",
    "    x1 = F.tanh(x1) # 使用 PyTorch 自带的 tanh 激活函数\n",
    "    x2 = torch.mm(x1, w2) + b2\n",
    "    return x2\n",
    "\n",
    "optimizer = torch.optim.SGD([w1, w2, b1, b2], 1.)\n",
    "\n",
    "criterion = nn.BCEWithLogitsLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\envs\\mytorch\\lib\\site-packages\\torch\\nn\\functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1000, loss: 0.27227547764778137\n",
      "epoch: 2000, loss: 0.26514551043510437\n",
      "epoch: 3000, loss: 0.2603071331977844\n",
      "epoch: 4000, loss: 0.25685396790504456\n",
      "epoch: 5000, loss: 0.2542983889579773\n",
      "epoch: 6000, loss: 0.25234663486480713\n",
      "epoch: 7000, loss: 0.2508157193660736\n",
      "epoch: 8000, loss: 0.24958734214305878\n",
      "epoch: 9000, loss: 0.24858269095420837\n",
      "epoch: 10000, loss: 0.2477470338344574\n"
     ]
    }
   ],
   "source": [
    "# 我们训练 10000 次\n",
    "for e in range(10000):\n",
    "    # 前向传播\n",
    "    out = two_network(Variable(x))\n",
    "    loss = criterion(out, Variable(y))\n",
    "    \n",
    "    # 反向传播\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # 计算精度\n",
    "    if (e + 1) % 1000 == 0:\n",
    "        print('epoch: {}, loss: {}'.format(e+1, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_network(x):\n",
    "    x = Variable(torch.from_numpy(x).float())\n",
    "    x1 = torch.mm(x, w1) + b1\n",
    "    x1 = F.tanh(x1)\n",
    "    x2 = torch.mm(x1, w2) + b2\n",
    "    out = F.sigmoid(x2)\n",
    "    out = (out > 0.5) * 1\n",
    "    return out.data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda3\\envs\\mytorch\\lib\\site-packages\\torch\\nn\\functional.py:1806: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2 layer network')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(lambda x: plot_network(x), x.numpy(), y.numpy())\n",
    "plt.title('2 layer network')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到神经网络能够非常好地分类这个复杂的数据，和前面的 logistic 回归相比，神经网络因为有了激活函数的存在，成了一个非线性分类器，所以神经网络分类的边界更加复杂。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequential 和 Module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们讲了数据处理，模型构建，loss 函数设计等等内容，但是目前为止我们还没有准备好构建一个完整的机器学习系统，一个完整的机器学习系统需要我们不断地读写模型。在现实应用中，一般我们会将模型在本地进行训练，然后保存模型，接着我们会将模型部署到不同的地方进行应用，所以在这节课我们会教大家如何保存 PyTorch 的模型。\n",
    "\n",
    "首先我们会讲一下 PyTorch 中的模块，Sequential 和 Module。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "对于前面的线性回归模型、 Logistic回归模型和神经网络，我们在构建的时候定义了需要的参数。这对于比较小的模型是可行的，但是对于大的模型，比如100 层的神经网络，这个时候再去手动定义参数就显得非常麻烦，所以 PyTorch 提供了两个模块来帮助我们构建模型，一个是Sequential，一个是 Module。\n",
    "\n",
    "Sequential 允许我们构建序列化的模块，而 Module 是一种更加灵活的模型定义方式，我们下面分别用 Sequential 和 Module 来定义上面的神经网络。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sequential\n",
    "seq_net = nn.Sequential(\n",
    "    nn.Linear(2, 4), # PyTorch 中的线性层，wx + b\n",
    "    nn.Tanh(),\n",
    "    nn.Linear(4, 1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=2, out_features=4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 序列模块可以通过索引访问每一层\n",
    "\n",
    "seq_net[0] # 第一层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "-0.4964  0.3581\n",
      "-0.0705  0.4262\n",
      " 0.0601  0.1988\n",
      " 0.6683 -0.4470\n",
      "[torch.FloatTensor of size 4x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 打印出第一层的权重\n",
    "\n",
    "w0 = seq_net[0].weight\n",
    "print(w0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过 parameters 可以取得模型的参数\n",
    "param = seq_net.parameters()\n",
    "\n",
    "# 定义优化器\n",
    "optim = torch.optim.SGD(param, 1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1000, loss: 0.2839296758174896\n",
      "epoch: 2000, loss: 0.2716798782348633\n",
      "epoch: 3000, loss: 0.2647360861301422\n",
      "epoch: 4000, loss: 0.26001378893852234\n",
      "epoch: 5000, loss: 0.2566395103931427\n",
      "epoch: 6000, loss: 0.2541380524635315\n",
      "epoch: 7000, loss: 0.25222381949424744\n",
      "epoch: 8000, loss: 0.2507193386554718\n",
      "epoch: 9000, loss: 0.24951006472110748\n",
      "epoch: 10000, loss: 0.2485194206237793\n"
     ]
    }
   ],
   "source": [
    "# 我们训练 10000 次\n",
    "for e in range(10000):\n",
    "    out = seq_net(Variable(x))\n",
    "    loss = criterion(out, Variable(y))\n",
    "    optim.zero_grad()\n",
    "    loss.backward()\n",
    "    optim.step()\n",
    "    if (e + 1) % 1000 == 0:\n",
    "        print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，训练 10000 次 loss 比之前的更低，这是因为 PyTorch 自带的模块比我们写的更加稳定，同时也有一些初始化的问题在里面，关于参数初始化，我们会在后面的课程中讲到"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_seq(x):\n",
    "    out = F.sigmoid(seq_net(Variable(torch.from_numpy(x).float()))).data.numpy()\n",
    "    out = (out > 0.5) * 1\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x118abf5f8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111fb9400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(lambda x: plot_seq(x), x.numpy(), y.numpy())\n",
    "plt.title('sequential')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后我们讲一讲如何保存模型，保存模型在 PyTorch 中有两种方式，一种是将模型结构和参数都保存在一起，一种是只将参数保存下来，下面我们一一介绍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将参数和模型保存在一起\n",
    "torch.save(seq_net, 'save_seq_net.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面就是保存模型的方式，`torch.save`里面有两个参数，第一个是要保存的模型，第二个参数是保存的路径，读取模型的方式也非常简单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 读取保存的模型\n",
    "seq_net1 = torch.load('save_seq_net.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=2, out_features=4)\n",
       "  (1): Tanh()\n",
       "  (2): Linear(in_features=4, out_features=1)\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_net1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      " -0.5532  -1.9916\n",
      "  0.0446   7.9446\n",
      " 10.3188 -12.9290\n",
      " 10.0688  11.7754\n",
      "[torch.FloatTensor of size 4x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(seq_net1[0].weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看到我们重新读入了模型，并且将其命名为 seq_net1，并且打印了第一层的参数\n",
    "\n",
    "下面我们看看第二种保存模型的方式，只保存参数而不保存模型结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 保存模型参数\n",
    "torch.save(seq_net.state_dict(), 'save_seq_net_params.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过上面的方式，我们保存了模型的参数，如果要重新读入模型的参数，首先我们需要重新定义一次模型，接着重新读入参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_net2 = nn.Sequential(\n",
    "    nn.Linear(2, 4),\n",
    "    nn.Tanh(),\n",
    "    nn.Linear(4, 1)\n",
    ")\n",
    "\n",
    "seq_net2.load_state_dict(torch.load('save_seq_net_params.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=2, out_features=4)\n",
       "  (1): Tanh()\n",
       "  (2): Linear(in_features=4, out_features=1)\n",
       ")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_net2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      " -0.5532  -1.9916\n",
      "  0.0446   7.9446\n",
      " 10.3188 -12.9290\n",
      " 10.0688  11.7754\n",
      "[torch.FloatTensor of size 4x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(seq_net2[0].weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过这种方式我们也重新读入了相同的模型，打印第一层的参数对比，发现和前面的办法是一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有这两种保存和读取模型的方法，我们推荐使用**第二种**，因为第二种可移植性更强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们再用 Module 定义这个模型，下面是使用 Module 的模板\n",
    "\n",
    "```\n",
    "class 网络名字(nn.Module):\n",
    "    def __init__(self, 一些定义的参数):\n",
    "        super(网络名字, self).__init__()\n",
    "        self.layer1 = nn.Linear(num_input, num_hidden)\n",
    "        self.layer2 = nn.Sequential(...)\n",
    "        ...\n",
    "        \n",
    "        定义需要用的网络层\n",
    "        \n",
    "    def forward(self, x): # 定义前向传播\n",
    "        x1 = self.layer1(x)\n",
    "        x2 = self.layer2(x)\n",
    "        x = x1 + x2\n",
    "        ...\n",
    "        return x\n",
    "```\n",
    "\n",
    "注意的是，Module 里面也可以使用 Sequential，同时 Module 非常灵活，具体体现在 forward 中，如何复杂的操作都能直观的在 forward 里面执行\n",
    "\n",
    "下面我们照着模板实现一下上面的神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class module_net(nn.Module):\n",
    "    def __init__(self, num_input, num_hidden, num_output):\n",
    "        super(module_net, self).__init__()\n",
    "        self.layer1 = nn.Linear(num_input, num_hidden)\n",
    "        \n",
    "        self.layer2 = nn.Tanh()\n",
    "        \n",
    "        self.layer3 = nn.Linear(num_hidden, num_output)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mo_net = module_net(2, 4, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear(in_features=2, out_features=4)\n"
     ]
    }
   ],
   "source": [
    "# 访问模型中的某层可以直接通过名字\n",
    "\n",
    "# 第一层\n",
    "l1 = mo_net.layer1\n",
    "print(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      " 0.1492  0.4150\n",
      " 0.3403 -0.4084\n",
      "-0.3114 -0.0584\n",
      " 0.5668  0.2063\n",
      "[torch.FloatTensor of size 4x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 打印出第一层的权重\n",
    "print(l1.weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义优化器\n",
    "optim = torch.optim.SGD(mo_net.parameters(), 1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1000, loss: 0.2618132531642914\n",
      "epoch: 2000, loss: 0.2421271800994873\n",
      "epoch: 3000, loss: 0.23346386849880219\n",
      "epoch: 4000, loss: 0.22809192538261414\n",
      "epoch: 5000, loss: 0.224302738904953\n",
      "epoch: 6000, loss: 0.2214415818452835\n",
      "epoch: 7000, loss: 0.21918588876724243\n",
      "epoch: 8000, loss: 0.21736061573028564\n",
      "epoch: 9000, loss: 0.21585838496685028\n",
      "epoch: 10000, loss: 0.21460506319999695\n"
     ]
    }
   ],
   "source": [
    "# 我们训练 10000 次\n",
    "for e in range(10000):\n",
    "    out = mo_net(Variable(x))\n",
    "    loss = criterion(out, Variable(y))\n",
    "    optim.zero_grad()\n",
    "    loss.backward()\n",
    "    optim.step()\n",
    "    if (e + 1) % 1000 == 0:\n",
    "        print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 保存模型\n",
    "torch.save(mo_net.state_dict(), 'module_net.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到我们得到了相同的结果，而且使用 Sequential 和 Module 来定义模型更加方便\n",
    "\n",
    "在这一节中我们还是使用梯度下降法来优化参数，在神经网络中，这种优化方法有一个特别的名字，反向传播算法，下一次课我们会讲一讲什么是反向传播算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**小练习：改变网络的隐藏层神经元数目，或者试试定义一个 5 层甚至更深的模型，增加训练次数，改变学习率，看看结果会怎么样**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面举个例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net = nn.Sequential(\n",
    "    nn.Linear(2, 10),\n",
    "    nn.Tanh(),\n",
    "    nn.Linear(10, 10),\n",
    "    nn.Tanh(),\n",
    "    nn.Linear(10, 10),\n",
    "    nn.Tanh(),\n",
    "    nn.Linear(10, 1)\n",
    ")\n",
    "\n",
    "optim = torch.optim.SGD(net.parameters(), 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1000, loss: 0.3165791928768158\n",
      "epoch: 2000, loss: 0.25367119908332825\n",
      "epoch: 3000, loss: 0.22129501402378082\n",
      "epoch: 4000, loss: 0.20364265143871307\n",
      "epoch: 5000, loss: 0.19186729192733765\n",
      "epoch: 6000, loss: 0.18199527263641357\n",
      "epoch: 7000, loss: 0.173702672123909\n",
      "epoch: 8000, loss: 0.16727975010871887\n",
      "epoch: 9000, loss: 0.16238373517990112\n",
      "epoch: 10000, loss: 0.15855807065963745\n",
      "epoch: 11000, loss: 0.15542374551296234\n",
      "epoch: 12000, loss: 0.1527201235294342\n",
      "epoch: 13000, loss: 0.15030623972415924\n",
      "epoch: 14000, loss: 0.14812862873077393\n",
      "epoch: 15000, loss: 0.1461697667837143\n",
      "epoch: 16000, loss: 0.14440736174583435\n",
      "epoch: 17000, loss: 0.14280635118484497\n",
      "epoch: 18000, loss: 0.1413293182849884\n",
      "epoch: 19000, loss: 0.13908402621746063\n",
      "epoch: 20000, loss: 0.13768813014030457\n"
     ]
    }
   ],
   "source": [
    "# 我们训练 20000 次\n",
    "for e in range(20000):\n",
    "    out = net(Variable(x))\n",
    "    loss = criterion(out, Variable(y))\n",
    "    optim.zero_grad()\n",
    "    loss.backward()\n",
    "    optim.step()\n",
    "    if (e + 1) % 1000 == 0:\n",
    "        print('epoch: {}, loss: {}'.format(e+1, loss.data[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10abaf518>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a9d38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_net(x):\n",
    "    out = F.sigmoid(net(Variable(torch.from_numpy(x).float()))).data.numpy()\n",
    "    out = (out > 0.5) * 1\n",
    "    return out\n",
    "\n",
    "plot_decision_boundary(lambda x: plot_net(x), x.numpy(), y.numpy())\n",
    "plt.title('sequential')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
